The research team framed their inquiry around a well-established model from the user experience literature: the User Experience Honeycomb, originally proposed by information architect Peter Morville. The honeycomb decomposes experience into seven facets—Usefulness, Usability, Desirability, Findability, Accessibility, Credibility, and Value—each representing a distinct question a user implicitly asks of a product. Is it useful? Can I use it easily? Do I want it? Can I find what I need? Can I access it? Do I trust it? Does it deliver worth? While the framework has been applied to websites, apps, and consumer electronics, it had never been empirically deployed in the context of wardrobe-integrated lighting, a product category that sits at the intersection of furniture design, interior architecture, and the Internet of Things.

Methodologically, the study adopted a mixed-methods design. The researchers conducted qualitative interviews to surface the lived frustrations and delights of users, then followed up with a questionnaire survey targeting young adults, a demographic the authors identify as the core early-adopter group for smart home furnishings. Interview participants provided written informed consent, including consent for audio recording and transcription, and the human-participant component of the study received ethics approval from Nanjing Forestry University’s Animal Welfare and Ethics Committee in February 2024. All data were anonymized, and participants could withdraw at any time—a procedural detail that matters in a study whose findings center on users’ concerns about data handling.

From the survey responses, factor analysis—a statistical technique that identifies latent dimensions underlying a set of observed variables—extracted five core experience dimensions: Usability, Findability, Value, Credibility, and Accessibility. In plain terms, users’ experiences with smart wardrobe lighting cluster around whether the system is easy to operate, whether they can quickly locate the lighting functions and garments they need, whether the product feels worth its cost, whether they trust it, and whether it accommodates people across abilities and situations. Notably, the analysis of this particular product context did not yield the full seven honeycomb facets as separable dimensions; instead, the facets compressed into these five empirically distinct factors, suggesting that in embedded furniture systems, some experiential qualities merge in users’ minds.

The study then went a step further than most user experience research by quantifying how much each dimension matters. Using the Criteria Importance Through Inter-criteria Correlation method—known as CRITIC—a weighting technique that derives the objective importance of criteria from the statistical correlations and variability among them, the researchers assigned weights to the experience dimensions. The result was unexpected: Desirability and Usefulness, the emotional and functional poles of the honeycomb, carried the highest importance. Users, in other words, care most about whether the lighting is appealing and whether it genuinely serves a purpose—not merely whether the interface is polished or the app is responsive.

From these converging findings, the authors distilled what they call the Efficacy–Access Gap. Although users’ day-to-day interactions with smart wardrobe lighting are primarily guided by functional dimensions—Usability, Findability, Accessibility—their ultimate satisfaction hinges on something different: whether the lighting effects actually meet situational needs. A wardrobe light can be flawlessly operable and easy to navigate, yet if its color rendering makes a navy blazer look black, or if its brightness cannot adapt to a dim bedroom at six in the morning, the user walks away dissatisfied. The gap describes a mismatch between the dimensions that shape behavior and the dimension that shapes judgment, and it offers a diagnostic lens for why technically competent smart furniture products can still fail commercially.

Equally consequential is the study’s finding on Credibility. Users expressed concerns over data security and system integration—whether the wardrobe is quietly collecting information about their habits, and whether it will actually interoperate with the other devices in their homes. The researchers found that credibility functions as a foundational condition for long-term engagement: without trust, even the most beautifully lit wardrobe will not retain its user. This positions security and integration not as afterthoughts to be patched in firmware updates, but as prerequisites that must be embedded from the earliest stages of design, alongside the optics and the sensors.

The practical guidance that flows from the study is unusually concrete for user experience research. The authors recommend that designers prioritize usability and findability as the levers that unlock perceived value—make the system effortless to operate and easy to navigate, and users will begin to feel the product is worth having—while simultaneously embedding credibility from day one. For an industry racing to furnish homes with connected everything, the message is that the path to perceived value runs through operational simplicity, and the path to retention runs through trust. The Efficacy–Access Gap adds a third instruction: validate lighting performance against real situational needs, such as early-morning dressing in low ambient light, rather than against laboratory benchmarks.

Why does a wardrobe light deserve this level of scrutiny? Because the wardrobe is a uniquely revealing testbed for smart furniture. It is a confined, frequently used space where lighting quality directly affects a daily ritual—choosing what to wear—and where color fidelity, glare control, and sensor-triggered behavior are experienced at close range, often in dim surroundings. It is also a product where the electronics are hidden inside furniture, meaning failures are felt as failures of the wardrobe itself. If user experience principles can be quantified and prioritized in this demanding micro-environment, the same framework plausibly extends to illuminated mirrors, smart closets, and the broader class of health-oriented home furnishings the research group’s funding projects target.

The study’s originality, as the authors state it, lies in three contributions: applying the User Experience Honeycomb to an underexplored product context, empirically quantifying the weights of experience dimensions rather than assuming them, and identifying a context-specific gap between interaction drivers and satisfaction determinants. Published open access in Humanities and Social Sciences Communications, the work extends user experience theory beyond screens and into furniture, and it hands designers a prioritized checklist grounded in data. As smart home technology seeps into every cabinet and closet, the research suggests the winning products will not be the ones with the most features, but the ones that are easy to use, easy to navigate, trustworthy, and—above all—genuinely effective in the moments that matter.

Subject of Research: User experience of smart wardrobe lighting systems

Article Title: User experience of smart wardrobe lighting systems: identifying pain points and prioritizing core elements

Article References: Zhou, C., Wang, X., Qian, Y., & Kaner, J. (2026). User experience of smart wardrobe lighting systems: identifying pain points and prioritizing core elements. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09359-2

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09359-2

Keywords: smart wardrobe lighting, user experience, User Experience Honeycomb, CRITIC weighting method, factor analysis, usability, findability, credibility, data security, smart furniture, human-centered design, Internet of Things

The study, published in the journal Cluster Computing, introduces what the authors call EQR-MCPABE, short for an efficient and quantum-resistant multi-authority ciphertext-policy attribute-based encryption scheme. The work was carried out by Sravya Gudipati and R. Padmavathy of the National Institute of Technology Warangal, together with Syam Kumar Pasupuleti of the Quantum-Safe Cryptography Lab at the Institute for Development and Research in Banking Technology in Hyderabad. Gudipati and Pasupuleti contributed equally to the research.

Attribute-based encryption is a family of techniques that rethinks who holds the keys to a piece of data. Instead of encrypting a file for one specific recipient, the data owner encrypts it under a policy written in terms of attributes, such as department, clearance level, or role. Only users whose credentials satisfy the policy can decrypt the content. In the ciphertext-policy variant, the encryptor dictates the policy, which makes the approach a natural fit for fine-grained access control in cloud environments where files may be read by many different people over long periods.

Multi-authority versions of the scheme add another layer of practicality. Rather than relying on a single trusted authority to issue every key, a dangerous single point of failure, multiple independent authorities each manage their own set of attributes. A hospital might have one authority for medical roles and another for administrative clearances, and no single entity can reconstruct a user’s full secret key. This decentralization has made multi-authority ciphertext-policy attribute-based encryption, or MCP-ABE, one of the most promising building blocks for privacy-preserving cloud applications.

Yet nearly all of the classical MCP-ABE schemes rest on number-theoretic assumptions, problems such as factoring and computing discrete logarithms that Shor’s algorithm, running on a future quantum computer, is known to solve efficiently. That looming vulnerability has pushed cryptographers toward lattice-based constructions, whose security rests on problems like Learning with Errors that have resisted both classical and quantum attack strategies for nearly two decades. Lattice problems involve finding short vectors in high-dimensional geometric structures, and no known quantum algorithm breaks them.

Existing lattice-based MCP-ABE schemes, however, carry a heavy computational burden. Most are built on the Learning with Errors problem or its ring-based variant, Ring-LWE, and they rely on Gaussian sampling algorithms, procedures that draw random values from a bell-shaped distribution over a lattice. Gaussian sampling is notoriously expensive and delicate to implement correctly, and it has historically been a major source of inefficiency in lattice cryptography. On top of that, many of these schemes lack effective attribute revocation, the ability to cleanly remove a user’s access when attributes change, which is essential for any system handling dynamic organizations where people join, leave, and change roles constantly.

The new scheme sidesteps both problems. EQR-MCPABE is constructed from module lattices, a middle ground between general lattices and ideal lattices that offers a balance of security confidence and efficiency. Module lattices have gained prominence in post-quantum cryptography; the standardized key-encapsulation mechanism CRYSTALS-Kyber, selected by standards bodies for quantum-safe encryption, is itself built on module lattices. Crucially, the new construction avoids Gaussian sampling algorithms altogether, which the authors identify as the key to reducing computation cost while preserving resistance to quantum attacks.

Revocation is handled through a server-assisted mechanism. Instead of forcing every authority to reissue keys whenever an attribute must be revoked, the scheme delegates part of the revocation workload to the cloud server, allowing access to be cut off efficiently without imposing a heavy burden on users or authorities. The design achieves both forward and backward security, meaning that a revoked user cannot read data encrypted after the revocation, and cannot recover data encrypted before it either, closing the window that poorly designed revocation systems often leave open.

On the theoretical side, the authors prove that EQR-MCPABE is secure against chosen-plaintext attacks under the Module-LWE assumption, the module-lattice analogue of the Learning with Errors problem. Chosen-plaintext security is a standard formal guarantee: an adversary who can obtain encryptions of messages of their choosing still cannot extract information about other encrypted content. Grounding the proof in Module-LWE ties the scheme’s security to a well-studied lattice problem, aligning it with the assumptions underpinning the leading post-quantum standards.

Performance analysis presented in the paper shows that the scheme outperforms existing lattice-based MCP-ABE constructions, a result the researchers attribute largely to the elimination of Gaussian sampling and the efficiency of the module-lattice setting. As organizations worldwide begin the long migration to quantum-safe cryptography, work like this points toward access-control systems that can protect fine-grained, multi-authority cloud data not just against today’s attackers, but against the quantum machines of tomorrow, without the performance penalties that have kept earlier lattice-based designs out of practical deployment.

Subject of Research: A quantum-resistant multi-authority attribute-based encryption scheme for secure cloud storage

Article Title: Efficient and quantum-resistant multi-authority CP-ABE with attribute revocation for cloud Storage

Article References: Gudipati, S., Pasupuleti, S. K., & Padmavathy, R. (2026). Efficient and quantum-resistant multi-authority CP-ABE with attribute revocation for cloud Storage. Cluster Computing, 29(15), Article 849. https://doi.org/10.1007/s10586-026-06660-2

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06660-2

Keywords: cryptography, post-quantum cryptography, lattice-based cryptography, cloud storage, attribute-based encryption, Module-LWE, attribute revocation, access control, multi-authority encryption, quantum computing, data security, Efficient

The core insight of the research is that existing approaches tend to treat these technologies separately. Blockchain frameworks excel at providing decentralized trust, transparency, and tamper-resistant record-keeping, but they lack the intelligence to detect threats in real time. Machine learning models can identify anomalies and classify risks with impressive accuracy, but they typically operate on centralized data stores that are themselves susceptible to manipulation and single points of failure. Encryption schemes like AES and RSA safeguard data in transit and at rest, but most implementations do not integrate decentralized verification or intelligent threat detection. The result, according to Al-Abassy, is a fragmented security landscape in which each technology addresses only part of the problem while leaving critical gaps in confidentiality, integrity, privacy, and trust.

To close these gaps, the proposed framework deploys a lightweight Random Forest classifier as its first line of defense. Before any file is encrypted or registered on the blockchain, the classifier examines file-level features such as size, type, and extension, assigning each file to one of three risk categories: Low, Medium, or High. This classification is not merely academic. It determines the level of protection each file receives, allowing the system to apply adaptive security rather than a one-size-fits-all approach. Common document formats and non-sensitive files are flagged as Low risk, executable formats and moderately sensitive content as Medium risk, and encrypted archives, highly sensitive medical records, or files with multiple security-sensitive properties as High risk. The classifier was trained on an internally generated dataset of 10,000 file samples, stratified into 4,000 Low-risk, 3,000 Medium-risk, and 3,000 High-risk categories, with 80 percent used for training and 20 percent reserved for independent testing.

Once a file has been classified, the framework applies AES–RSA hybrid encryption to secure it. AES, the Advanced Encryption Standard, is a symmetric cipher prized for its speed and efficiency in encrypting large volumes of data. RSA, named after its inventors Rivest, Shamir, and Adleman, is an asymmetric algorithm that excels at secure key exchange and authentication. In the hybrid scheme, AES handles the bulk encryption of the file content, while RSA encrypts and distributes the AES session keys, ensuring that even if an attacker intercepts the communication, the encrypted content cannot be retrieved without the proper decryption key. This division of labor is what gives hybrid encryption its power: AES provides high-speed protection for the data itself, while RSA guarantees that the keys protecting that data are exchanged securely.

After encryption, the framework registers the file on a lightweight private blockchain. Only essential metadata is stored on the ledger—file identifiers, timestamps, access details, and SHA-512 hash values—while the encrypted files themselves reside in cloud storage. Each block on the chain includes the timestamp, transaction details, current hash, and previous hash, creating a chronological, tamper-resistant record of every file’s lifecycle. A permissioned validation mechanism limits participation in transaction verification to approved entities, reducing the computational overhead that plagues public blockchains while maintaining the decentralized integrity verification, traceability, and immutable auditing that blockchain provides. The result is a system in which no single party can unilaterally alter the record of what was shared, when, and by whom.

The experimental results are striking. Across heterogeneous datasets containing files of different sizes and types, the framework achieved an average encryption time of 1.68 milliseconds, an average decryption time of 0.63 milliseconds, and an average blockchain verification time of 2.75 milliseconds. The Random Forest classifier reached 99 percent accuracy in multi-class risk classification, with precision, recall, and F1-score all averaging 99 percent across the three risk categories. Blockchain-based integrity verification achieved 100 percent accuracy. Every single security operation—whether encryption, decryption, or blockchain verification—completed in under 0.01 seconds, a threshold the researcher identifies as essential for real-time smart environment applications. Statistical analysis across thirty independent experimental runs confirmed the consistency of these results, with p-values below 0.001 for all measured metrics.

File-level analysis revealed that processing time scales with file size rather than file type. A small 120-kilobyte CSV dataset required just 1.21 milliseconds to encrypt, while a 5,400-kilobyte executable file took 2.03 milliseconds. A 6,800-kilobyte binary firmware file, classified as High risk due to its potentially dangerous format, required 2.45 milliseconds. This direct proportional relationship between file size and processing time—expressed mathematically as T ∝ S—demonstrates that the encryption and decryption algorithms handle data regardless of format, and that the system remains stable and effective across the full spectrum of digital content found in smart environments.

Ablation studies, in which individual components were systematically removed while the rest of the system remained intact, confirmed that the framework’s strength lies in the complementary contributions of its parts. Removing the AI classification module eliminated adaptive risk assessment. Replacing AES–RSA hybrid encryption with a baseline technique reduced the level of cryptographic protection. Removing the blockchain layer eliminated immutable auditability and traceability. Switching from SHA-512 to SHA-256 hashing modified the integrity verification method. In every case, the full configuration offered the most complete set of security, integrity, traceability, and intelligent risk-assessment features, demonstrating that the framework relies not on any single technique but on the synergy of all four.

Comparative analysis against existing security frameworks highlighted the balanced trade-off the proposed system achieves. Traditional IoT security frameworks relying solely on AES encryption reached only 65 percent detection accuracy because they lack intelligent detection capabilities. Machine learning-based intrusion detection systems improved accuracy to 78 percent but offered no encryption or decentralized trust layer. Blockchain-based storage security solutions provided strong data integrity but suffered from high computational overhead, with encryption times reaching 3.50 milliseconds. Deep learning security models achieved 85 percent accuracy but lacked blockchain-based integrity protection. The proposed framework, by contrast, combines 99 percent detection accuracy with millisecond-scale encryption, decryption, and verification times, all while maintaining full file and data integrity through SHA-512 hashing.

The implications extend beyond the laboratory. As smart cities, intelligent healthcare systems, and industrial IoT networks continue to expand, the need for security architectures that can guarantee confidentiality, integrity, privacy, and trust simultaneously becomes ever more urgent. The framework’s computational complexity analysis shows that each component scales efficiently: AI-based classification operates at O(n × f), AES encryption and decryption at O(m), SHA-512 hashing at O(m), and blockchain verification at O(b), where n represents the number of processed files, f the extracted features, m the file size, and b the blockchain verification operations. Al-Abassy suggests that future work will explore lightweight blockchain consensus mechanisms, edge intelligence, federated learning, privacy-preserving techniques, and post-quantum cryptography to further enhance scalability, resilience, and real-time secure data sharing. For now, the research offers a compelling demonstration that when artificial intelligence, hybrid cryptography, and blockchain are woven together rather than deployed in isolation, the result is a security architecture that is simultaneously intelligent, scalable, and trustworthy.

Subject of Research: An AI-driven blockchain framework with AES–RSA hybrid encryption for secure data sharing in IoT smart environments

Article Title: An AI-based blockchain framework with AES–RSA hybrid encryption for secure data sharing in smart environments

Article References: AbdulKareem Al-Abassy, S. (2026). An AI-based blockchain framework with AES–RSA hybrid encryption for secure data sharing in smart environments. Frontiers in Blockchain, 9, Article 1935632. https://doi.org/10.3389/fbloc.2026.1935632

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1935632

Keywords: artificial intelligence, blockchain, AES, RSA, hybrid encryption, Internet of Things, smart environments, data security, anomaly detection, cybersecurity, AI-based, framework

The work, authored by Rupa Jamatia and Bubu Bhuyan of the Department of Information Technology, addresses one of the oldest tensions in the field. Steganography differs from encryption in a crucial way: cryptography scrambles a message so that intercepted text is unreadable, but the interception itself is obvious. Steganography instead hides the existence of the message. A secret payload is embedded inside an innocuous cover object, in this case a color image, so that anyone observing the traffic sees only an ordinary photograph. The price of that concealment is fragility. If the embedding process disturbs the cover image too much, statistical artifacts appear, and modern steganalysis tools can flag the image as suspicious. The art lies in embedding as much data as possible while leaving the smallest possible fingerprint.

The most straightforward way to hide bits in an image is least significant bit, or LSB, replacement. In a typical 24-bit color image, each pixel stores one byte each for its red, green and blue intensities. Flipping the lowest-order bit of a byte changes the intensity by at most one unit out of 255, a difference far below what the human eye can perceive. Sequential LSB embedding, however, has a well-known weakness: if the sender fills pixel after pixel in raster order, the hidden data occupies a predictable, contiguous region of the image. Steganalysis tools exploit exactly this regularity, scanning for zones where the statistical behavior of the least significant bits deviates from natural image noise. Randomizing where the bits land is therefore a central defense, and it is the pivot of the new study.

Jamatia and Bhuyan’s framework uses the XORShift64 generator, a pseudorandom number generator introduced by George Marsaglia in 2003, to decide which pixels receive secret bits. XORShift64 belongs to a family of generators prized for their extreme simplicity: the state is updated by a handful of bitwise exclusive-OR and shift operations, which modern processors execute in a few nanoseconds. The authors emphasize that the generator itself is a commonly used primitive; their contribution lies in how it is integrated into the embedding strategy. By seeding the generator and using its output to select pixel positions unpredictably, the scheme ensures that an adversary who does not know the seed cannot reconstruct the sequence of modified pixels, and the scattered, irregular pattern of changes minimizes the detectability of the resulting stego image. The computational overhead, they note, is minimal, which matters for practical deployment on constrained devices.

Capacity is the second pillar of the method. Rather than embedding a single bit per pixel, the XS-RPVI approach writes message bits into the least significant bits of all three color channels, red, green and blue, achieving three bits per pixel. That tripling of payload means a cover image can carry substantially more secret data before the sender must reach for a larger photograph, and larger covers themselves can attract attention. The trade-off, of course, is that modifying three bits per pixel distorts the cover more than modifying one, so the scheme must demonstrate that the resulting images still look and measure as natural. The authors report that the approach balances imperceptibility, visual fidelity and embedding capacity, preserving the perceptual quality of the original images even at the full three-bit payload.

Measuring that preservation requires standardized image quality metrics, and the study leans on the established toolkit of the field. Peak signal-to-noise ratio, or PSNR, quantifies the numerical difference between the cover and stego images, with higher values indicating smaller distortion; values above roughly 40 decibels are conventionally considered evidence that modifications are visually negligible. The structural similarity index, SSIM, goes further by modeling how human vision judges changes in luminance, contrast and structure, while the feature similarity index, FSIM, compares low-level visual features. The literature the authors cite, including comparative studies of these metrics and analyses of their use specifically in steganography evaluation, reflects a consensus that imperceptibility must be assessed both numerically and perceptually, since a stego image that passes one metric can still fail another.

Imperceptibility alone is not enough, because a hidden message that survives human scrutiny may still betray itself to statistical analysis. The authors evaluated their scheme against three classic detection tests. Visual inspection confirmed that stego images show no discernible artifacts. Histogram analysis examined the distribution of pixel intensities: natural images have characteristic, often smooth histograms, and naive embedding tends to produce telltale pairing effects in which adjacent intensity values converge in frequency. Chi-square distance tests, rooted in the statistical method Karl Pearson developed in the nineteenth century, compare the observed distribution of least significant bit values against the expectations of a natural image, flagging images whose bit planes look artificially uniform. The experimental evaluations showed that the proposed method withstands detection through all three tests, and comparative analyses with existing techniques further validated its robustness and reliability in concealing information while preserving visual fidelity.

The design choices sit within a rich research lineage that the paper’s bibliography traces in detail. Early combinations of cryptography and pseudorandom numbers for steganography date back more than a decade, and subsequent work refined random pixel selection, explored interpolation-based embedding, pursued lowest-distortion LSB schemes, and brought in error-correcting codes such as Hamming codes to raise capacity adaptively. More recent contributions have applied genetic algorithms to cover image selection, combined random pixel and bit selection for high payloads, and embedded encrypted text within color images using randomized pixel ordering. Parallel work in the same journal has explored chaotic maps for image ciphering and new high-capacity, high-security steganographic schemes. Against this backdrop, the Shillong study’s use of a lightweight, well-understood generator like XORShift64 is a pragmatic argument: security through unpredictability does not require exotic mathematics, only a well-integrated source of pseudorandomness and careful attention to where the bits go.

The practical implications extend to any scenario in which the confidentiality of transmitted images is paramount. The authors point to sensitive image exchange and information protection as the applications where the technique’s strong concealment capability and computational efficiency show the most promise. In an era when the paper’s own framing describes escalating cybersecurity threats driven by increasingly sophisticated attack techniques and a growing reliance on the internet for extensive data sharing, covert channels embedded in ordinary media offer a complementary layer of defense: even if an encrypted file is intercepted, an observer cannot be sure that a routine photograph does not also carry a hidden payload. The method’s low computational cost makes it suitable for embedding and extraction on everyday hardware, and its three-bit-per-pixel capacity reduces the number of cover images needed for a given volume of secret data.

Like all spatial-domain LSB methods, the approach inherits the field’s standing caveats. The study reports resistance to visual, histogram and chi-square analyses, but the steganalysis arms race continues, with machine-learning detectors constantly improving at recognizing even faint embedding traces, and robustness against image transformations such as compression or resizing remains a separate question from undetectability. The authors’ contribution is nonetheless a clear demonstration of a principle that has governed information hiding since Claude Shannon laid the mathematical foundations of communication theory in 1948: uncertainty in the mind of the adversary is a resource, and it can be manufactured cheaply. By spending a few bitwise operations per pixel to decide where each secret bit lands, the XS-RPVI framework turns one of the oldest tricks in digital steganography into a harder target for the tools that hunt it, and it does so while leaving the photograph, to every eye and every standard test, exactly what it seems.

Subject of Research: Image steganography using XORShift64-based random pixel selection for secure data hiding in color images

Article Title: Image steganography: emphasizing bit encoding positions for secure communication

Article References: Jamatia, R., & Bhuyan, B. (2026). Image steganography: emphasizing bit encoding positions for secure communication. Multimedia Tools and Applications, 85(10), Article 806. https://doi.org/10.1007/s11042-026-21962-8

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21962-8

Keywords: steganography, image steganography, LSB embedding, XORShift64, pseudorandom number generator, information hiding, steganalysis, chi-square test, PSNR, SSIM, data security, spatial domain

The research, led by Yong Wang of Wannan Medical University together with colleagues at Anhui Normal University, Bengbu University and Zhejiang University, introduces a scheme called UDSQ, short for user-defined dynamic skyline secure query. At its heart lies a classic problem from multi-criteria database theory known as the skyline query. Given a collection of data points, each described by several attributes, a skyline query returns the set of points that are not dominated by any other point. A point dominates another if it is at least as good in every attribute and strictly better in at least one. In a medical context, imagine searching a database of patient cases for records that minimize both treatment cost and symptom severity while maximizing treatment success. The skyline consists of all cases that no other case beats across that entire combination of criteria, giving clinicians a shortlist of genuinely compelling matches rather than an arbitrary ranking.

What makes the new work distinctive is the word dynamic in its title. In conventional skyline systems, the attributes used for comparison are fixed in advance by the database designer. A patient consulting an online diagnosis service, however, may care about very different things: one might prioritize the experience level of the treating physician, another the proximity of the clinic, a third the cost of medication. UDSQ allows the user to define the region of interest, the set of dimensions and the thresholds that matter to them at query time. The system then restricts the skyline computation to that user-defined region, which has a welcome side effect: by shrinking the search space, it reduces the computational burden and the amount of data that must travel back and forth between the server and the client.

The central obstacle, of course, is that the medical records on the server are supposed to be encrypted. A skyline query appears to require exactly what encryption forbids: comparing values to determine which point dominates which. The authors resolve this tension by combining two cryptographic primitives. The first is order-revealing encryption, a technique that allows a server to compare the relative order of two ciphertexts without being able to decrypt either one. With order-revealing encryption, the server can evaluate dominance relationships among encrypted records, determining whether one encrypted case is better than another across the user’s chosen attributes, while learning nothing about the actual values of cost, severity or success rate.

The second primitive is proxy re-encryption, which addresses a different problem: how a single untrusted server can process queries from many users without each user having to share a secret key with the server. In a proxy re-encryption scheme, a data owner can generate a special transformation key that lets the server convert ciphertexts encrypted under one key into ciphertexts readable under another, without the server ever seeing the plaintext or the original key. In UDSQ, this mechanism allows the diagnosis platform to re-encrypt results so that only the querying patient can read them. Crucially, the authors show that combining proxy re-encryption with a single-server model defends against collusion attacks, scenarios in which a malicious server and a compromised user pool their information in an attempt to reconstruct other patients’ data. Because the transformation keys are structured to prevent any coalition from inverting the encryption, the scheme maintains its privacy guarantees even under this stronger adversary model.

To make the encrypted skyline computation efficient, the researchers designed two dedicated algorithms. The Rectangles Intersection Determination Algorithm, or RIDA, represents the user-defined query region geometrically and determines which candidate data points fall within its boundaries by computing intersections between rectangular regions in the attribute space. Rather than testing every record against every dimension individually, RIDA prunes the candidate set using rectangle intersection tests performed directly on encrypted representations. The Skyline Point Determination Algorithm, or SPDA, then takes the surviving candidates and establishes their dominance relationships in ciphertext, identifying which points belong to the final skyline. The division of labor matters: by separating spatial pruning from dominance testing, the pipeline avoids the quadratic comparisons that plague naive skyline implementations, and the authors report that the combination yields measurable advantages in both communication and computation overhead compared with existing approaches.

The experimental evaluation, conducted on publicly available benchmark datasets rather than real patient records, supports the efficiency claims. Because the study did not involve human participants or new human subject data, the authors note that ethics approval was not applicable, and the use of public benchmarks means the performance figures reflect the algorithms themselves rather than any particular clinical deployment. The reported results indicate that UDSQ reduces the communication cost between client and server, a critical factor for patients in areas with limited bandwidth, while also lowering the computational load on the server. In encrypted-query research, where cryptographic operations typically impose orders-of-magnitude slowdowns, achieving both security and competitive overhead is the benchmark of a practical design.

The implications extend beyond medicine. Skyline queries arise wherever users must choose among multi-attribute alternatives: hotel bookings balancing price, location and rating; investment portfolios balancing risk and return; sensor networks balancing energy consumption and data fidelity. Any of these domains could adopt the user-defined, encrypted paradigm that UDSQ demonstrates. But medicine is the most urgent case, because the stakes of disclosure are highest. A patient’s diagnostic history, once leaked, cannot be changed the way a compromised password can. Regulatory frameworks around the world increasingly demand that health data be protected not only in transit but also during computation, and techniques like order-revealing encryption represent one of the few viable paths to computing on data that remains sealed throughout the process.

Still, the scheme is not a panacea, and honest reporting requires acknowledging the boundaries of what the study demonstrates. Order-revealing encryption, while powerful, leaks the ordering of values by design, and the cryptographic community continues to study what auxiliary information an adversarial server might infer from such leakage when combined with background knowledge. The single-server model simplifies deployment but concentrates trust in one infrastructure provider, even if that provider cannot read the data. And the user-defined nature of the query, while empowering, means that the privacy profile can shift with each new query configuration, a dynamic that future work will need to characterize formally. The authors’ contribution is best understood as a well-engineered point in the design space, one that balances these tensions more favorably than prior schemes while leaving open questions that the field is actively pursuing.

What the study ultimately signals is a maturing of privacy-preserving computation from theoretical curiosity toward deployable infrastructure. The researchers, funded by the doctoral research foundation of Wannan Medical College and the Anhui Province Intelligent Robot Information Fusion and Control Engineering Research Center, have published their work open access, allowing clinicians, cryptographers and system builders to scrutinize and extend it. As online diagnosis platforms expand into regions where they can do the most good, the demand for systems that never force patients to choose between accurate answers and protected data will only grow. UDSQ offers a concrete demonstration that this choice is not inevitable: with the right combination of user control, geometric pruning and modern encryption, the skyline of the best possible medical matches can be computed entirely in the dark, and delivered intact to the one person who holds the key.

Subject of Research: Secure user-defined dynamic skyline queries for privacy-preserving online medical diagnosis

Article Title: UDSQ: a secure and efficient user-defined dynamic skyline query in online medical diagnosis

Article References: Wang, Y., Xu, X., Nie, X., & Peng, G. (2026). UDSQ: a secure and efficient user-defined dynamic skyline query in online medical diagnosis. Journal of Big Data. https://doi.org/10.1186/s40537-026-01581-8

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01581-8

Keywords: skyline query, data security, privacy protection, online medical diagnosis, order-revealing encryption, proxy re-encryption, encrypted queries, cloud computing, database management, collusion attacks, health informatics, cryptography

The scheme, developed by El Mahdi Mouloua and colleagues, rests on two novel operators that work in tandem. The first is a chaotic polycyclic shift, denoted by the Greek letter phi with a chaotic vector index, which generalizes the familiar left and right bit rotations used in classical cryptography. Instead of shifting data by a fixed amount, the operator displaces pixels and their bits by amounts dictated by a chaotic sequence, so that the pattern of movement never repeats and cannot be predicted without the secret key. This operator drives the confusion mechanism of the cipher, scrambling the relationship between the original image and its encrypted form.

The second operator addresses the other pillar of strong encryption: diffusion. In cryptographic terms, confusion hides the connection between the key and the ciphertext, while diffusion ensures that a change to a single element of the plaintext spreads throughout the entire encrypted output. The researchers call their diffusion engine the chaotic avalanche effect, symbolized by xi. It creates a strong binding between each encrypted pixel and the following original pixel, so that information from one part of the image cascades into the encryption of subsequent parts. A single flipped bit in the input therefore triggers an avalanche of changes across the ciphertext, denying attackers the ability to modify localized regions of an image undetected.

The intellectual roots of the work reach into an unexpected corner of mathematics: the theory of polycyclic codes. One of the co-authors, Oussama Kabbouch, had previously explored the algebraic structure of polycyclic codes with collaborators, building on earlier generalizations of cyclic codes introduced by Lopez-Permouth, Parra-Avila and Szabo in 2009. By importing the cyclic shift idea from coding theory into the chaotic domain, the team transformed a static algebraic operation into a dynamic, key-dependent one. The chaotic vector that steers the shift is generated by chaotic maps, whose extreme sensitivity to initial conditions means that even a minute difference in the key produces a completely different scrambling pattern.

Chaos theory has long fascinated cryptographers for precisely this reason. Chaotic systems, such as the logistic map and its more elaborate relatives like the intertwining logistic map, exhibit deterministic but seemingly random behavior. Their trajectories never settle into a pattern, yet they can be reproduced exactly by anyone holding the same initial values. This combination of reproducibility and unpredictability makes them natural candidates for generating the keystreams and permutation sequences that encryption demands. The new scheme taps this property to control both the polycyclic shifts and the avalanche diffusion, ensuring that every stage of the cipher is driven by the same chaotic engine and the same secret key material.

The experimental evaluation reported in the paper reads like a checklist of modern cryptographic benchmarks, and the scheme passes each one. The key space, which measures how many possible secret keys an attacker would need to brute-force, is described as large, a critical property given that many published chaotic ciphers have been broken because their key spaces proved too small. Key sensitivity is similarly high: encrypting the same image with keys that differ by a single digit yields entirely different ciphertexts, which prevents an attacker from gleaning information through near-miss keys.

Statistical tests on the encrypted images tell a similar story. The histograms of the encrypted outputs are uniform, meaning that every pixel value appears with equal frequency and no statistical fingerprint of the original image survives. The correlation between adjacent pixels, which in natural images is typically very high because neighboring pixels tend to look alike, drops to low values in the ciphertext, destroying the structural redundancy that attackers exploit in known-plaintext and chosen-plaintext attacks. Information entropy, a measure of randomness, reaches high values close to the theoretical maximum of eight bits per pixel for eight-bit images, indicating that the encrypted data is essentially indistinguishable from noise.

Two additional metrics, standard in the image encryption literature, quantify the scheme’s resistance to differential attacks. The number of pixels change rate, or NPCR, measures the fraction of pixels that change when the plaintext is altered by a single bit, while the unified average changing intensity, or UACI, measures the average strength of those changes. Ideal values are close to 99.6 percent and 33.4 percent respectively, and the reported strong encryption performance of the scheme, together with low peak signal-to-noise ratio values between original and encrypted images, indicates that the chaotic avalanche effect delivers the full diffusion strength needed to defeat these attacks. Low PSNR here is a virtue: it confirms that the encrypted image leaks essentially nothing about the original.

The broader context makes the contribution timely. The literature is littered with image encryption schemes that looked impressive on paper but collapsed under cryptanalysis, including DNA-based ciphers and designs built on the two-dimensional logistic-adjusted-sine map, both of which were broken by dedicated attacks. Meanwhile the demand for image security keeps growing, driven by telemedicine platforms transmitting diagnostic scans, the Industrial Internet of Things streaming visual sensor data, and cloud storage services holding vast archives of personal photographs. Recent research has explored hyperchaotic maps, memristive neural networks, quantum walks and DNA encoding as ingredients for next-generation ciphers, and the Moroccan-Romanian team positions its polycyclic approach within this competitive landscape, arguing that a mathematically grounded shift operator offers a cleaner and more principled confusion mechanism than ad hoc permutation tricks.

The researchers, who contributed equally to the work alongside co-authors from institutions in Morocco and Romania, suggest that the combination of a generalized cyclic structure with chaotic dynamics could extend beyond still images, potentially informing the protection of video streams and other multimedia content where efficiency and security must coexist. For now, the scheme stands as a demonstration that classical algebra and modern chaos theory can be fused into a cipher that is more than the sum of its parts: a system in which the ancient idea of the cyclic shift is reborn, steered by randomness that only the legitimate key holder can reproduce, and in which every pixel of an image guards the secrets of every other.

Subject of Research: Chaotic image encryption using polycyclic shift operators

Article Title: Design of a secure image encryption scheme using chaotic polycyclic shift

Article References: Mouloua, E. M., Ben-Hdech, A., Bejan, C. A., Es-Sabry, M., Najmeddine, M., Kabbouch, O., Bucerzan, D., & Essaid, M. (2026). Design of a secure image encryption scheme using chaotic polycyclic shift. Cluster Computing, 29(13), Article 771. https://doi.org/10.1007/s10586-026-06588-7

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06588-7

Keywords: image encryption, chaos theory, polycyclic codes, cryptography, confusion and diffusion, chaotic maps, multimedia security, NPCR, UACI, information entropy, Cluster Computing, data security

The motivation for the work begins with a fundamental mismatch. Standard encryption algorithms such as AES, DES and RSA were designed to protect streams of text or raw data, and they perform reasonably well on such inputs. Digital images, however, are different beasts. They are large, they contain highly redundant pixel information, and neighboring pixels tend to be strongly correlated with one another, often sharing values that differ by only a few bits. When conventional ciphers are applied directly to image data, the processing becomes slow and the underlying structure of the picture can leak through. Steganography, the art of hiding one file inside another, offers concealment but not true cryptographic resistance to cryptanalysis. For applications like medical imaging and military reconnaissance, where confidentiality is non-negotiable, researchers have therefore turned to specialized image encryption methods built around two classical operations: confusion, which rearranges the positions of pixels, and diffusion, which alters the values of pixels.

The first pillar of the new scheme is chaos theory. Chaotic systems are nonlinear dynamical processes in which a minuscule change in the starting conditions produces wildly divergent outcomes over time, a phenomenon popularly known as the butterfly effect. This extreme sensitivity, combined with apparent randomness, makes chaotic maps attractive for generating pseudo-random keystreams that depend on secret initial values. The particular tool chosen here is the Arnold Cat Map, a deceptively simple transformation that stretches and folds a two-dimensional space, named for the cartoon cat whose face becomes an unrecognizable blur when the map is applied repeatedly. In the encryption pipeline, the Cat Map acts as the confusion layer: it permutes the coordinates of every pixel according to a matrix equation involving two secret positive integers, m and n, applied modulo the image dimension N. In the reported experiments the team selected m and n equal to 7 and iterated the transformation four times. Because the map preserves pixel values while shuffling their locations, the picture retains its histogram but loses all recognizable geometry, appearing as pure noise.

The second pillar borrows from molecular biology. DNA cryptography exploits the four-letter alphabet of nucleotides, adenine, cytosine, guanine and thymine, along with the complementary base-pairing rules that govern how these letters bind together. In a computational setting, binary data can be encoded into quaternary DNA sequences, and operations such as substitution and exclusive OR can be performed according to the pairing rules A with T and C with G. The researchers begin with a chosen DNA sequence representing each nucleotide as an eight-bit binary string. From each string they extract pairs of bits, starting with the least significant two bits, then the next two, and so on, assembling these fragments into seed values. The resulting binary strings are concatenated, a designated bit is flipped through a significant-bit-changing strategy, and permutation selection produces four key strings. These are arranged into an eight-by-eight key matrix, which serves as the secret keystream for the diffusion stage. The DNA layer thus introduces an additional dimension of randomness and an enlarged key space beyond what the chaotic map alone provides.

The full encryption procedure weaves these components together in a carefully ordered sequence. First, the original image is passed through the Arnold Cat Map, which scatters pixel positions across the frame. Next, the shuffled image is divided into blocks of eight-by-eight pixels. Each block is then combined with the DNA-derived key matrix using the XOR operation, a bitwise comparison that outputs a one whenever the two input bits differ. This XOR step constitutes the diffusion layer, transforming pixel values rather than merely relocating them. Finally, all processed blocks are concatenated to form the encrypted image. Decryption simply reverses the pipeline: the cipher image is broken into blocks, XORed with the same key matrix to recover the shuffled picture, and the inverse Cat Map transformation restores the original pixel coordinates. Crucially, an adversary would need both the DNA sequence parameters and the Cat Map keys to reconstruct anything meaningful, a requirement that substantially raises the bar against brute-force attempts.

To evaluate the scheme, the team ran MATLAB 2019a simulations on an Intel Core i3-1005G1 processor with 6 gigabytes of RAM, testing the algorithm on a hand X-ray, a chest X-ray and the classic Baboon test image. The encrypted outputs were visually indistinguishable from random noise, and decryption with the correct keys restored the originals faithfully. The researchers measured encryption and decryption times across images of varying dimensions, finding that processing time scales with image size, an expected but practically important result for real-time or resource-constrained systems. They also note that changing the image dimension alters the period of the Cat Map, which in turn affects key size and the strength of the confusion effect, trading improved security against increased computational and memory costs.

Quantitative security metrics told a consistent story. The mean square error between original and encrypted images was high, while the peak signal-to-noise ratio, or PSNR, was correspondingly low. In image encryption, a low PSNR is a virtue rather than a flaw: it signals that the cipher image bears almost no resemblance to the source, which is precisely what a defender wants. Histogram analysis reinforced this conclusion. The histograms of the encrypted images differed dramatically from those of the originals and appeared uniformly distributed, denying an attacker the statistical fingerprints that leak information about the underlying content. Correlation coefficients between adjacent pixels, measured horizontally, vertically and diagonally, dropped to nearly zero after encryption, whereas natural images typically exhibit correlations close to one in all three directions. This near-total destruction of neighboring-pixel relationships is a hallmark of effective confusion.

Information entropy provided further evidence of robustness. For an eight-bit image, maximum theoretical entropy is eight bits, achieved when all 256 gray levels occur with equal probability. The encrypted images produced entropy values approaching 7.9980, edging out comparable schemes in the literature that reported values of 7.9966 and 7.9968. The team also subjected the method to differential attack analysis using two standard indices: the number of pixels change rate, NPCR, and the unified average changing intensity, UACI. The scheme achieved NPCR values of approximately 99 percent and UACI values near 33 percent, the theoretical benchmarks for resisting differential attacks. These figures mean that flipping a single pixel in the plaintext produces a completely different ciphertext, making chosen-plaintext and known-plaintext attacks extraordinarily difficult to mount.

The study positions itself within a broader research landscape in which one-dimensional chaotic maps have given way to richer two- and three-dimensional systems, and in which DNA operations are increasingly paired with chaos to compensate for their individual weaknesses. Earlier efforts combined chaotic maps with SHA-256 hashing, fractal structures, substitution boxes and hyperchaotic dynamics, each adding layers of complexity. The new contribution distills these lessons into a lightweight framework that pairs a well-understood permutation mechanism with biologically inspired key generation, achieving strong security without the computational overhead of multi-dimensional chaotic systems. The authors acknowledge that their future work will probe the scheme’s resistance to chosen-plaintext and known-plaintext attacks under varying ACM parameters and DNA sequences. For now, the results suggest that the humble tools of molecular biology, when married to the mathematics of chaos, can offer a surprisingly powerful shield for the images the world increasingly depends on keeping secret.

Subject of Research: Hybrid image encryption combining DNA cryptography and the Arnold Cat Map chaotic transformation

Article Title: Enhanced image encryption using a hybrid technique based on dna cryptography and arnold cat map

Article References: Bano, M., Iqbal, J., Habib, U., Hajjej, F., & Ullah, I. (2026). Enhanced image encryption using a hybrid technique based on dna cryptography and arnold cat map. Cluster Computing, 29(13), Article 770. https://doi.org/10.1007/s10586-026-06499-7

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06499-7

Keywords: image encryption, DNA cryptography, Arnold Cat Map, chaotic maps, XOR operation, cryptography, information entropy, PSNR, histogram analysis, NPCR, UACI, data security

The dominant way organizations let AI query their databases is a technique called Text-to-SQL. The language model receives the full schema definition of the database, including every table and column name, and then writes SQL queries directly against it. The problem is that this approach hands the model everything: cryptic column identifiers, proprietary encoding schemes, and business logic embedded in the schema itself. In a credit rating context, transmitting such details to an external API endpoint constitutes exactly the kind of cross-boundary data transfer that network segregation laws are designed to prevent. The proposed alternative is a Semantic Layer, an abstraction interface borrowed from business intelligence architecture that sits between the model and the database, exposing only curated business concepts such as dimensions and measures while supposedly keeping raw schema details hidden inside the organization.

The intellectual pedigree of this idea traces back to David Parnas’s celebrated 1972 principle of Information Hiding, which holds that software modules should conceal their internal implementation details behind minimal, stable interfaces. In theory, a Semantic Layer instantiates this principle at the AI boundary: the model sees concepts like region and average salary, never the underlying physical columns they map to. But Yang’s study delivers a striking and cautionary finding. Whether the abstraction actually hides anything depends on an implementation detail that a schema-level analysis alone cannot reveal: whether the mapping from business concepts back to physical SQL is itself included in the prompt sent to the model.

The initial implementation examined in the study, labeled v1, looked entirely reasonable on paper. Its prompt contained elegant, human-readable concept definitions in which raw identifiers like A3 and A11 were replaced with meaningful names. But the same prompt also included a SQL mapping reference translating each concept back to its physical expression, along with a listing of physical table and column names needed to compose valid joins. When Yang performed an exhaustive verification of the complete payload actually transmitted to the model, checking every token across all benchmark questions, the result was damning: all fifteen sensitive column identifiers that the concept layer was nominally designed to hide were present in the prompt. Measured at the payload level, v1 achieved precisely zero reduction in schema exposure compared with raw Text-to-SQL.

The corrected implementation, v2, takes a fundamentally different approach. The model receives only concept names, their descriptions, and concept-level join relationships, and is instructed to respond using abstract concept references rather than physical SQL. A local compiler, running inside the organization and never exposed to the model, resolves those references into physical SQL after the API response returns. Verified exhaustively across the full benchmark, v2 removes all fifteen designated schema identifiers from the LLM-facing payload. The lesson Yang draws is pointed: information hiding at an AI interface is achieved by the complete prompt an implementation sends, not by the presence of concept-level names somewhere within it, and verifying it requires payload-level auditing rather than inspection of the abstraction layer’s design in isolation.

What about accuracy, the other half of the dilemma? Here the results are more nuanced and, in places, genuinely surprising. The study evaluated three conditions, Text-to-SQL, Semantic Layer v1, and v2, across three model tiers spanning a capability range, on two benchmarks. On a purpose-built credit-rating benchmark of sixty questions across six credit risk categories, designed alongside the Semantic Layer specification itself, v2’s accuracy improved steadily with model capability, rising from 38.7 percent at the weakest tier to 60.7 percent at the strongest, where it was numerically the highest of the three conditions, ahead of v1 at 58.7 percent and Text-to-SQL at 52.0 percent. Yang is careful to note that with only sixty questions, this strongest-tier advantage does not reach statistical significance, so the corrected architecture should be read as matching, not definitively beating, its baselines while additionally achieving full schema-identifier removal.

The picture changes dramatically on an externally authored benchmark, the financial subset of the well-known BIRD dataset, evaluated against the same underlying Czech banking schema. There, v2 trailed both baselines at every single model tier, and the gap between the in-specification benchmark and the external one widened as model capability increased. Yang attributes this deficit largely to genuine limitations of the v2 compiler and specification when confronted with question types they were never designed to handle, rather than to simple implementation defects. The author names this pattern selective encapsulation: some concepts benefit enormously from abstraction, particularly those with opaque encodings like single-character loan status codes, while others, such as already-transparent date fields, gain nothing from an extra layer of indirection. Crucially, it is presented as an empirical design lesson rather than a predictive theory.

The failure analysis contains perhaps the most operationally consequential insight of the entire study. At the weakest model tier, nearly half of v2’s outputs failed to compile or execute at all, a highly visible failure mode that falls steeply as capability rises, dropping to just 11 percent at the strongest tier. But the rate of wrong-but-executable results, queries that run successfully yet return incorrect answers, moved in the opposite direction, reaching over a third of generations at the stronger tiers. Unlike compile errors, these silent failures cannot be caught automatically before a result reaches an analyst. In other words, upgrading to a more capable model reduces the visible failure rate while leaving, or even increasing, the invisible one, a counterintuitive trap for any organization deploying these systems in high-stakes settings.

A component ablation added further practical texture. Removing few-shot examples from the v2 prompt caused a significant accuracy decline and nearly doubled the compile-error rate, while removing natural-language business descriptions made no significant difference. The few-shot examples, not the richness of the concept descriptions, emerged as the primary driver of the corrected architecture’s accuracy. Yang also withdrew two claims from the original submission: a purported theoretical extension of Information Hiding, which merely restated a standard engineering requirement, and a characterization of the results as evidence for the so-called Jagged Frontier of AI capabilities, since a smooth decline in accuracy with task difficulty does not establish the irregular capability boundary that concept actually describes.

The study’s practical message for regulated financial institutions is deliberately conditional rather than promotional. A correctly implemented Semantic Layer can achieve full schema-level information hiding without a detectable aggregate accuracy cost, but only when the specification is designed and maintained against the target query workload, and the benefit does not transfer automatically to new question distributions, even on the same database. Yang recommends treating specification design as an ongoing engineering task, including a task-type audit of which query categories are adequately covered, prioritizing compiler engineering for the join-path resolution failures that persist across all model tiers, and establishing human-review protocols specifically aimed at catching wrong-but-executable results. To enable independent scrutiny, the author has released the first domain-specific natural-language-to-SQL benchmark for credit rating contexts, complete with gold queries and an execution-based self-check, alongside all code and consolidated results. Whether these findings, obtained on a single schema with models from a single vendor, extend to proprietary rating databases and other model families remains an open question, but the core warning stands: at the AI boundary, security is not what your architecture looks like, it is what your payload actually contains.

Subject of Research: Semantic Layer architectures for reducing schema identifier exposure in LLM-based database querying in credit rating contexts

Article Title: A semantic layer architecture for removing schema identifiers from external LLM prompts, with model-dependent accuracy trade-offs

Article References: Yang, M. (2026). A semantic layer architecture for removing schema identifiers from external LLM prompts, with model-dependent accuracy trade-offs. Discover Artificial Intelligence, 6(1), Article 1374. https://doi.org/10.1007/s44163-026-02147-6

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02147-6

Keywords: semantic layer, large language models, Text-to-SQL, information hiding, schema identifier exposure, credit rating agencies, data security, selective encapsulation, BIRD benchmark, prompt architecture, model-dependent accuracy, AI compliance

The scheme, named CNNHSIE, short for Convolutional Neural Network and Hyperchaotic System-based Image Encryption, is built around three core components. The first is a hyperchaotic system, a class of dynamical systems that exhibit not just one but multiple positive Lyapunov exponents, meaning their trajectories diverge exponentially in several independent directions at once. This extreme sensitivity to initial conditions makes hyperchaotic sequences ideal for generating pseudo-random matrices that look statistically indistinguishable from true noise to any observer who does not possess the exact initial values. In CNNHSIE, the hyperchaotic system produces the random matrix that drives both the scrambling of pixel positions and the alteration of pixel values, two operations that cryptographers call permutation and diffusion.

Permutation and diffusion are the twin pillars of image encryption. Permutation rearranges the positions of pixels so that the spatial structure of the original image is destroyed, while diffusion changes the actual pixel values so that even a single-bit change in the plaintext cascades through the entire ciphertext. Traditional schemes often perform these operations in separate, sequential passes, which can be computationally expensive and can leave exploitable structure behind. The Iranian researchers tackled this bottleneck with two novel techniques. The first, called HSPT for high-speed permutation technique, reorders pixels with an efficiency that dramatically reduces encryption time compared with conventional sorting-based or swapping-based permutation strategies. The second, DPDT for dynamic pixel diffusion technique, modifies pixel values in a way that adapts dynamically during the encryption process rather than following a fixed pattern, making it substantially harder for an attacker to model or reverse the transformation.

What sets CNNHSIE apart from many previous chaos-based cryptosystems is its incorporation of a convolutional neural network into the key-generation process. Specifically, the authors employ the VGG16 architecture, a deep convolutional network originally developed for large-scale image classification, to extract features from the plain image itself. These extracted features are then used to compute the public key. This design choice has an elegant consequence: the encryption key material is tied to the unique statistical fingerprint of each individual image, so even minor differences between two plaintext images lead to different key components. An attacker who obtains the ciphertext for one image gains no reusable shortcut for decrypting another, because the network-derived key material is image-specific and changes from one input to the next.

The researchers evaluated CNNHSIE rigorously using standard test images from the USC-SIPI image database, a widely used benchmark repository maintained by the University of Southern California, along with public datasets drawn from Radiopaedia, an open-access radiology resource, and Kaggle, the popular machine-learning data platform. The inclusion of medical imaging datasets is significant, because medical images are among the most sensitive classes of data transmitted over networks, and regulatory frameworks around the world impose strict confidentiality requirements on them. A scheme that performs well on both natural photographic images and clinical scans has a broader practical reach than one tested only on generic benchmarks.

The headline results are striking. For the standard 1024 by 1024 pixel test image known as Male, CNNHSIE achieved a unified average changing intensity, or UACI, of 33.4809 percent, a number that sits almost exactly at the theoretical ideal of 33.46 percent for a perfectly random-looking ciphertext. The number of pixels change rate, or NPCR, reached 99.6140 percent, meaning that when the encryption key was perturbed by even a minuscule amount, more than 99.6 percent of all ciphertext pixels changed value, the hallmark of extreme key sensitivity. The information entropy of the encrypted image measured 7.9998 bits out of a theoretical maximum of 8 bits for an 8-bit image, indicating that the distribution of pixel values in the ciphertext is essentially uniform and carries virtually no exploitable information about the original.

Correlation analysis told a similarly compelling story. Adjacent pixels in a natural image are almost always highly correlated, because neighboring pixels tend to have similar brightness values. Effective encryption must drive this correlation to zero in every direction, horizontal, vertical and diagonal. CNNHSIE produced ciphertexts with near-zero correlation coefficients across all tested directions, confirming that the permutation and diffusion stages thoroughly destroyed the statistical structure of the plaintext. Perhaps most importantly for real-world deployment, the scheme achieved these security metrics while encrypting the large 1024 by 1024 Male image in just 0.1216 seconds, a speed that places it firmly in the territory of real-time applications such as live video streaming, secure telemedicine consultations and on-the-fly transmission of surveillance imagery.

The authors report that CNNHSIE demonstrated the ability to resist a variety of common attacks, including the chosen-plaintext and known-plaintext attacks that plague weaker image cryptosystems. In a chosen-plaintext attack, an adversary deliberately encrypts specially crafted images to probe the internal workings of the algorithm; image-specific key material derived from the VGG16 feature extractor makes such probing far less informative, because the relationship between plaintext and key changes with every input. The combination of hyperchaotic pseudo-randomness, dynamic diffusion and neural key generation creates multiple independent layers of defense, so a weakness in any single layer does not compromise the whole system. High key sensitivity further ensures that brute-force attempts to guess the chaotic initial conditions face an astronomically large search space with no smooth gradient to follow.

This work sits within a rapidly growing research frontier that merges deep learning with chaos-based cryptography. Earlier efforts have explored DNA encoding schemes combined with fractional-order hyperchaotic systems, Latin square constructions, Joseph traversal and discrete Fourier transforms, and several groups have experimented with neural networks for key generation or ciphertext post-processing. Zefreh himself has a track record in this area, with prior publications on Latin square-based encryption and parallel diffusion-confusion cryptosystems. What distinguishes the new contribution is the explicit pairing of a proven, off-the-shelf feature extractor with high-speed permutation and dynamic diffusion, achieving a balance of security metrics and raw throughput that many earlier designs pursued separately rather than simultaneously.

The practical implications extend across the sectors where image confidentiality matters most. In healthcare, secure transmission of radiological images could protect patient privacy without adding perceptible latency to clinical workflows. In military and defense contexts, real-time encryption of reconnaissance imagery could reduce the window of vulnerability during transmission. In finance, encrypted document images could move through cloud infrastructure with stronger guarantees against tampering. The researchers note that data and code are available upon request from the corresponding author, which opens the door for independent verification and adoption by other laboratories. As digital image traffic continues its exponential growth, schemes like CNNHSIE, which treat speed and security as joint requirements rather than competing trade-offs, are likely to shape the next generation of multimedia security systems, and the marriage of chaotic dynamics with deep neural networks may prove to be one of the defining patterns of that evolution.

Subject of Research: A CNN and hyperchaotic system-based image encryption algorithm using high-speed permutation and dynamic diffusion

Article Title: CNNHSIE: CNN and hyperchaotic-based image encryption algorithm using high-speed permutation and dynamic diffusion

Article References: Zarei Zefreh, E., & Heydari, M. (2026). CNNHSIE: CNN and hyperchaotic-based image encryption algorithm using high-speed permutation and dynamic diffusion. Neural Computing and Applications, 38(17), Article 722. https://doi.org/10.1007/s00521-026-12419-y

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12419-y

Keywords: image encryption, convolutional neural network, hyperchaotic system, VGG16, permutation, diffusion, cryptography, information entropy, NPCR, UACI, medical imaging security, deep learning

The scale of the problem is not trivial. Unlike human healthcare, which operates under the Health Insurance Portability and Accountability Act in the United States and the General Data Protection Regulation in Europe, veterinary medicine has no equivalent overarching regulatory structure. Only 35 of the 50 US states impose specific regulations on protected animal information, and each implements them differently. The authors note that veterinary organizations have pursued no broad-ranging data security measures beyond financial privacy rules such as the Payment Card Industry Data Security Standard. The consequences are measurable: an estimated 11,000 smaller veterinary practices are attacked each year, and the average cyber claim costs around $135,000, with attacks on large academic institutions likely to incur escalating costs as cyberattacks on major human medical centers continue to rise.

What makes veterinary data uniquely difficult is the web of relationships surrounding each record. A single animal may have different owners over time, requiring careful handling of ownership information and consent for data sharing. Collaborative cancer care multiplies the number of practices, veterinarians, and caregivers involved, creating numerous points of vulnerability. The authors also distinguish between short-term clinical encounters, such as surgery for a stifle injury with follow-up rechecks, and the long-term client-patient-practitioner relationships typical of oncology, where data security and data use frameworks become central. Species diversity compounds the challenge, since desexing status, differing laboratory parameters, and species-specific conditions all shape how records must be structured and interpreted.

The fragmentation has real scientific costs. Despite growing dataset sizes in veterinary studies, the usage of informatics and big data approaches has not grown in step with human medicine, largely because established data-sharing frameworks are missing. Many practices, including large academic hospitals with full diagnostic, surgical, medical, and radiation oncology services, still maintain servers on-premises behind firewalls. Smaller clinics often lack the resources to manage complex data stores at all, and may need to turn to subscription-based software-as-a-service arrangements, in which case the authors recommend that practitioners specify data privacy guidelines in plain language before signing agreements. The COVID-19 pandemic offered a partial template, showing the value of independent intermediary bodies processing inputs from large and small contributors under consistent privacy practices, though veterinary medicine lacks the governing bodies and resources that made that effort possible.

At the technical core of the new framework are three pillars: encryption, access control, and anonymization. Encrypting electronic health records and client information both at rest and in transit prevents unauthorized access, while role-based authentication and user permission protocols limit data availability to authorized personnel. Anonymization techniques, such as stripping identifying information from research datasets and diagnostic images, allow data to be used for research without exposing owners. The authors highlight a marked gap between the de-identification standards applied to human and animal data, noting that there is sparse regulation on how veterinary data should be redacted when shared between institutions. In oncology, where rare cancers may cluster in specific towns or postal codes, generalizing epidemiological information to regional or state level may be necessary to achieve privacy protections comparable to human studies.

Cloud storage emerges as a pragmatic recommendation. Many veterinary practices in the United States still operate with paper records or on-premise servers, but cloud vendors often provide better security models than most organizations can achieve alone, thanks to dedicated teams and advanced data centers. The authors are careful to invoke the shared responsibility model, however: the cloud provider secures the infrastructure, but the institution remains responsible for securing its data and configurations within it. Novel approaches such as time-stamping authorities and blockchain-based methods can improve security against external attacks and maintain records of access to sensitive information, while regular backups, disaster recovery plans, and secure transmission channels such as virtual private networks and secure data portals offer safer alternatives to ordinary email.

Yet hardware and software are not the weakest link. Human error accounts for 88 to 95 percent of data breach incidents, with common failures including misdelivered sensitive data, weak passwords, phishing attacks, and lost or stolen devices. The authors argue that comprehensive staff training and periodic security updates are essential, citing evidence that even a short security course significantly improves an employee’s ability to identify phishing and false links. Regular audits, clear institutional privacy policies, and engagement with legal counsel round out the compliance picture, promoting transparency and accountability within veterinary healthcare institutions.

When data leaves the institution, as it routinely must in cancer care and multi-center research, the framework calls for formal data-sharing agreements that delineate terms of use, access rights, duration, and strict disclosure conditions. The authors draw a sharp distinction between commercial and academic partners. Commercial entities typically leverage data for market research and product development, often aggregating records from multiple centers through software products, and may seek exclusive ownership rights, requiring stringent confidentiality agreements. Academic institutions prioritize research, education, and open knowledge exchange, though the authors stress that ownership never means unrestricted access, and that data uses must always comply with confidentiality and consent agreements. To help smaller institutions that lack legal counsel, they propose that professional veterinary societies provide standardized boilerplate data-sharing agreements, and they recommend compliance audits by internal teams or independent third parties for large-scale arrangements.

Artificial intelligence raises the stakes considerably. The authors walk through the lifecycle of a single CT scan of a cat with a suspected nasal tumor, from raw projection data captured by the scanner, through reconstruction into DICOM files, migration to a picture archiving system, storage, retrieval, archiving, and eventual destruction, noting that veterinary-specific PACS may not adhere to HIPAA-style constraints such as automatic timeouts, individual logins, or secure image transmission. Feeding such heterogeneous records into AI pipelines demands preprocessing, labeling, and harmonization: one hospital’s diagnostic thresholds or terminology, such as labeling a tumor simply osteosarcoma versus appendicular osteosarcoma, can cause merged models to under- or over-predict risk. Standardized coding systems like SNOMED-VET CT could help, but are difficult to operationalize in routine care. Model performance also degrades through data shift and concept shift, where training populations differ from real-world deployment or the statistical properties of data change over time as treatments evolve.

The payoff, if these challenges are met, is substantial. In human medicine, radiology and radiation oncology AI products already dominate the FDA’s approved software-as-a-medical-device list, driven by digitalized foundations such as images, structures, and 3D dose distributions, and deep-learning auto-segmentation has recently been adapted for veterinary radiation planning. Multimodal machine learning that fuses imaging, pathology, genomics, and clinical data promises improved risk stratification and outcome prediction. The authors close with a call for standardized privacy regulations, data privacy education woven into veterinary curricula, and exploration of independent governed databases modeled on the National Surgical Quality Improvement Program, warning that regulation must be balanced against the real-world resource constraints of veterinary centers so that collaboration and innovation are not stifled. As AI moves deeper into clinical decision support and large language models begin synthesizing patient histories in veterinary practices, the question of who ingests, owns, and protects animal health data has become impossible to ignore.

Subject of Research: Data privacy and management frameworks for veterinary oncology records in the era of artificial intelligence

Article Title: Veterinary oncology data management in the era of artificial intelligence

Article References: Pu, S., Thompson, M., Ross, S., & Basran, P. S. (2025). Veterinary oncology data management in the era of artificial intelligence. Veterinary Oncology, 2(1), Article 29. https://doi.org/10.1186/s44356-025-00043-2

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00043-2

Keywords: veterinary oncology, data privacy, artificial intelligence, data security, data sharing, encryption, anonymization, cybersecurity, machine learning, radiation oncology, data governance, veterinary informatics