The project is led by principal investigator Edoardo Persichetti, Ph.D., a professor in FAU’s Department of Mathematics and Statistics and director of the university’s Center for Cryptology and Information Security within the Charles E. Schmidt College of Science. Co-principal investigator is Veronika Kuchta, Ph.D., an assistant professor in the same department. Together, the team will build the mathematical foundations for post-quantum protocols that offer advanced capabilities rather than just basic protection, aiming to strengthen digital security as quantum technology advances and to develop more efficient, scalable tools for safeguarding information in complex security applications.

To understand why this matters, it helps to look at how most of today’s public-key cryptography works. The security of the systems protecting online banking, messaging and commerce rests on mathematical problems that are easy to compute in one direction but extraordinarily hard to reverse with conventional computers, such as factoring enormous numbers or computing discrete logarithms. In 1994, mathematician Peter Shor showed that a sufficiently powerful quantum computer running his algorithm could solve those problems efficiently, which means a mature quantum machine could theoretically unravel the public-key infrastructure the digital world depends on. Post-quantum cryptography responds by building new methods on mathematical problems believed to remain hard even for quantum attackers.

Recent international efforts, including the standardization process run by the U.S. National Institute of Standards and Technology, have concentrated on the foundational primitives of that new arsenal: encryption schemes and digital signatures. But many emerging applications demand more sophisticated machinery. Decentralized systems, privacy-preserving technologies and multi-party protocols need cryptographic tools that do far more than encrypt a message or sign a document. The FAU research targets precisely this gap, designing and analyzing post-quantum protocols that can deliver advanced functions for applications where multiple parties must interact securely while protecting sensitive information and verifying the integrity of digital processes.

“This NSF support gives us an opportunity to take the next step in post-quantum cryptography by moving beyond basic encryption and signatures toward more advanced protocols that can address the increasingly complex security needs of emerging technologies,” said Persichetti. “The grant will allow us to develop new cryptographic constructions, rigorously analyze their security and investigate how they can be made more efficient and scalable. It also will help us train the next generation of researchers in an area that will be increasingly important as quantum computing advances.”

The technical heart of the project lies in algebraic structures drawn from several mathematical frameworks, including lattices, error-correcting codes, group actions and isogeny-based cryptography. Lattices, for instance, underpin schemes whose security rests on the difficulty of finding the shortest vector in a high-dimensional grid, a problem with no known efficient quantum solution. Code-based cryptography, meanwhile, leans on the decades-old difficulty of decoding arbitrary linear error-correcting codes, while isogeny-based approaches exploit the intricate structure of mappings between elliptic curves. By working across these families, the researchers can compare trade-offs in key sizes, computational cost and security assumptions, and combine their strengths in hybrid constructions.

On top of these foundations, the team will investigate protocols supporting advanced cryptographic functions such as blind signatures, threshold signatures, multisignatures and verifiable random functions. A blind signature allows someone to obtain a signed document without the signer seeing its contents, a property crucial for private electronic voting and anonymous credentials. Threshold signatures distribute signing power among several parties so that only an authorized subset acting together can produce a valid signature, eliminating single points of failure. Multisignatures compress the approvals of many participants into one compact object, and verifiable random functions generate randomness that anyone can check was computed honestly, a building block for blockchain consensus and secure lotteries. Making all of these quantum-resistant, efficient and scalable is a formidable mathematical challenge.

A vital component of the work will be the development of rigorous security proofs grounded in well-defined computational assumptions, the gold standard that separates genuine cryptographic guarantees from wishful thinking. The researchers will also employ zero-knowledge proofs, which let one party convince another that a statement is true without revealing any information beyond the truth of the statement itself, along with group-action frameworks that provide a clean algebraic language for constructing such proofs. The emphasis throughout will be on improving efficiency, security and scalability, because a protocol that is provably secure but too slow or too bulky for real systems protects nothing in practice.

The award builds on FAU’s growing national leadership in the field. Persichetti has played a significant role in preparing cryptographic systems for the quantum era: he submitted four algorithms to NIST’s post-quantum cryptography competition, three of which advanced to the final round, and his Hamming Quasi-Cyclic (HQC) algorithm was ultimately selected by NIST for standardization as part of its Post-Quantum Cryptography project. HQC, a code-based scheme, is designed to provide secure key exchange while protecting communications against attacks from future quantum computers. Notably, FAU is the only university in the United States represented among the authors of the two key-encapsulation mechanisms NIST selected for standardization, HQC and ML-KEM, and FAU faculty have also contributed to the CRYSTALS-DILITHIUM digital signature algorithm, another NIST selection. “This new NSF award builds directly on the expertise and research foundation we have developed through our work in post-quantum cryptography,” said Persichetti. “The selection of HQC for standardization demonstrated the importance of developing cryptographic systems that can withstand quantum threats. This project expands that effort into advanced functionalities that will be needed to protect increasingly sophisticated digital systems in the years ahead.”

Beyond the mathematics, the grant carries an investment in people. The project will support training and mentoring students through research experiences and outreach activities, cultivating the next generation of cryptographers and cybersecurity researchers who will confront security challenges that do not yet fully exist. “This award reflects Florida Atlantic’s growing strength at the intersection of mathematics, cryptography, cybersecurity and emerging technologies,” said Valery Forbes, Ph.D., dean of the Charles E. Schmidt College of Science. “Our faculty are not only contributing to the development of technologies designed to protect the nation’s digital infrastructure but are helping shape the next generation of post-quantum security. With nationally recognized research such as the work of Dr. Persichetti and our cryptography team, FAU is at the forefront of this rapidly evolving field.” The effort also aligns with FAU’s broader identity as Florida’s first quantum university: through its Center for Quantum Technologies, the institution unites faculty, students and industry partners to advance quantum research, and its Boca Raton campus now hosts a D-Wave Advantage2 annealing quantum computer, giving researchers hands-on access to leading-edge quantum hardware. As quantum computing inches closer to practical reality, work like this ensures that when the quantum era arrives, the mathematics defending the world’s data will already be waiting for it.

Subject of Research: Post-quantum cryptography protocol design funded by a National Science Foundation grant at Florida Atlantic University

Article Title: FAU receives $400,000 NSF grant to advance post-quantum cryptography

Article References: FAU receives $400,000 NSF grant to advance post-quantum cryptography. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: post-quantum cryptography, quantum computing, NSF grant, Florida Atlantic University, cryptography, lattices, error-correcting codes, zero-knowledge proofs, digital signatures, NIST standardization, cybersecurity, HQC

Tokenomics, the design science behind blockchain-based incentive systems, sits at the heart of this agenda. Rather than viewing a token merely as a digital asset to be traded, researchers in this field treat token structures, issuance rules, burn mechanisms and governance arrangements as the economic architecture of decentralized systems. When those mechanisms are well designed, they can coordinate the behavior of strangers at scale, circulating value through networks without a central intermediary. When they are poorly designed, they invite speculation, volatility and misaligned incentives that can undermine even the most technically sound platform. The new volume explores both sides of that equation across domains ranging from health insurance and intellectual property to community governance, food-waste recovery, agentic artificial intelligence and financial portfolio optimization.

One of the most striking contributions is an empirical, artificial-intelligence-based mapping of blockchain applications across the European Union public sector, compiled through the Public Sector Tech Watch observatory. The analysis examined 306 projects, assessing the degree of alignment between project descriptions and the 17 Sustainable Development Goals. The results show strong alignment with SDG 9, Industry, Innovation and Infrastructure, and SDG 17, Partnerships for the Goals, followed by more moderate alignment with SDG 8, Decent Work and Economic Growth. Meanwhile, goals such as SDG 2 on zero hunger, SDG 6 on clean water and sanitation, and SDG 14 on life below water remain weakly represented. For policymakers, the mapping offers a rare evidence base for judging how sustainability-oriented public sector innovation is actually being framed, and it highlights which goals may need deliberate attention if blockchain adoption in government is to serve the full breadth of the 2030 agenda.

The volume also takes a hard look at one of the most ambitious emerging models in the blockchain ecosystem: decentralized physical infrastructure networks, or DePIN. These networks use crypto-economic incentives to coordinate the crowdsourced deployment and operation of physical infrastructure, from wireless networks to energy systems. A review and research agenda on DePIN tokenomics identifies a recurring model the authors call the DePIN Flywheel, built on a Burn-and-Mint Equilibrium. In this arrangement, network demand is monetized through usage credits denominated in fiat currency, generated by burning the network’s native token. Combined with governance-adjusted token issuance and collateral requirements, this mechanism forms the economic core of DePIN networks. Yet the literature points to persistent challenges: token price volatility affects provider profitability, incentive alignment is difficult to maintain, non-speculative demand remains underdeveloped, and regulatory uncertainty looms over the entire model. Similar challenges are already observable in decentralized markets involving physical-world resources, including energy data.

In the domain of healthcare, a comprehensive study of blockchain-enabled tokenization for health insurance claims examines the challenges currently associated with claims management and the growing role of blockchain in strengthening trust within healthcare insurance systems. Reviewing the existing literature, the study identifies five fundamental components as central to the development of blockchain-based healthcare insurance solutions: smart contracts, identity tokens, claim tokens, the orchestration of off-chain medical data, and interoperability mechanisms. The technical logic is elegant. Smart contracts can encode claim rules directly on-chain, identity tokens can verify participants without exposing sensitive records, and claim tokens can represent the state of a claim as it moves through adjudication. But the hard problems lie off-chain, where medical data must be orchestrated across institutions, and interoperability mechanisms must bridge heterogeneous systems if tokenized claims are ever to function at scale.

Intellectual property receives an unexpectedly creative treatment in a study on thermodynamic control of patent tokenization. The research develops a framework for improving the efficiency of patent tokenization, thereby broadening opportunities for IP monetization and facilitating innovation aligned with the Sustainable Development Goals, including resilient infrastructure and climate-related technologies. Rather than depending on rigid, predetermined plans, the framework offers a more adaptable approach to innovation financing. More provocatively, the study demonstrates how thermodynamic reasoning can connect theoretical perspectives with practical applications in innovation finance, suggesting that universal variational principles observed in physical and biological systems may also provide a useful basis for understanding and structuring financial decision-making. It is a reminder that the mathematics of decentralized finance increasingly borrows from the deepest patterns in science itself.

At the community scale, a study on tokenomics design for local communities develops an interdisciplinary method for examining and designing socially embedded tokenized systems. The research proposes a participatory co-design methodology that stresses the importance of aligning token structures with community values, non-market forms of social integration, and the relational and context-dependent characteristics of money and digital artifacts. The methodology was preliminarily assessed through a case study focused on food-waste recovery, extending a research direction established in the previous volume on the token economy of community-oriented blockchain systems. The underlying insight is that tokens in local communities are not simply miniature currencies; they are social artifacts whose meaning depends on relationships, context and shared values, and co-design is essential if incentive mechanisms are to complement rather than corrode non-market forms of social integration.

Trust and privacy converge in a study that develops and validates a conceptual framework for applying zero-knowledge proof cryptographic primitives within the context of European regulatory requirements. Zero-knowledge proofs allow claims to be verified without requiring disclosure of the complete underlying dataset, a property with obvious value in legally sensitive settings. The study demonstrates the implementation of an agentic AI system designed to support pre-litigation analysis, then develops the concept of an agentic AI marketplace, identifying and describing the technological interactions that may occur within such an environment. Finally, the research formalizes demographic representation as a verifiable property that can be implemented across different technology stacks through smart contracts and oracles. In effect, fairness itself becomes an auditable, on-chain property, a technical achievement with significant implications for how AI ecosystems might be governed.

Financial applications round out the volume with an optimization analysis of cryptocurrency and European equity markets. The study demonstrates that cryptocurrencies such as Bitcoin and Ethereum offer substantial return potential, although their pronounced volatility also introduces considerable portfolio risk. In contrast, European equity indices exhibit relatively low correlations with other asset classes and can therefore contribute to greater portfolio stability. Diversification across these asset classes may consequently provide an effective means of reducing overall risk while maintaining opportunities for higher returns. The research also examines the role of blockchain-based financial products in expanding investment opportunities, highlighting their potential to broaden the range of assets available for portfolio construction, a sign that tokenized finance is moving from novelty toward a recognized asset class.

Collectively, the studies in this volume illustrate both the transformative potential of blockchain and the challenges associated with its implementation. They show increasing maturity and versatility in blockchain technology and tokenomics as mechanisms for supporting sustainable, transparent and decentralized innovation across public and private sectors, with the capacity to enhance operational efficiency, trust, security and stakeholder participation. They also reveal a notable shift in SDG alignment: the previous volume emphasized SDG 11 on sustainable cities, SDG 7 on clean energy, SDG 12 on responsible consumption and SDG 13 on climate action, while the current volume expands toward SDG 9, SDG 17 and SDG 8. That shift reflects the evolving scope of blockchain applications and suggests that ongoing technological developments are increasingly aligned with broader sustainable development objectives. At the same time, the findings emphasize the economic, governance and regulatory challenges that must be addressed for long-term adoption, and the delicate balance between promoting transformative innovation and maintaining economic and institutional stability. Effective implementation, the editors conclude, requires carefully designed tokenomic models, interoperability among technological infrastructures, compliance with regulatory frameworks, and effective alignment between technological development and society’s wider goals.

Subject of Research: Blockchain applications and tokenomics design for advancing the Sustainable Development Goals

Article Title: Editorial: Blockchain and tokenomics for sustainable development, volume II

Article References: Alexopoulos, C., Ferro, E., & Schifanella, C. (2026). Editorial: Blockchain and tokenomics for sustainable development, volume II. Frontiers in Blockchain, 9, Article 1952615. https://doi.org/10.3389/fbloc.2026.1952615

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1952615

Keywords: blockchain, tokenomics, Sustainable Development Goals, DePIN, smart contracts, zero-knowledge proofs, health insurance claims, patent tokenization, cryptocurrency portfolios, decentralized governance, Editorial, sustainable

The core insight of the research is that task scheduling and ledger consistency should not be treated as separate engineering problems. Traditional accounting platforms typically handle synchronization as an external reconciliation step that runs after execution has finished. Under bursts of concurrent traffic, that separation makes end-to-end performance sensitive to workload spikes, resource contention, dependency propagation, and delayed consistency confirmation. The new framework instead converts ledger synchronization status into a scheduling feedback signal, so that task priority, container allocation, dependency handling, and cross-ledger commit verification are all adjusted within the same execution loop.

The pipeline begins with an accounting task decomposer that partitions each incoming stream request, whether a transaction record or a ledger update, into validation, aggregation, and synchronization stages. These tasks are pushed into a distributed message queue where heterogeneous work is buffered and reordered according to system load. A scheduler continuously monitors YARN resource status, queue priority, and ledger synchronization urgency, dispatching validation and aggregation tasks to stream executor workers while routing cross-ledger commit operations to dedicated synchronization workers. A retry and backoff mechanism between the queue and the scheduler improves robustness when transient execution failures occur.

At the heart of the scheduling engine sits a deep reinforcement learning agent built on an actor-critic architecture. The state vector, 24 dimensions in total, concatenates queue statistics, cluster utilization ratios for CPU, memory, and I/O, task descriptors such as estimated execution time and historical latency, and ledger-related indicators including whether a task triggers cross-ledger synchronization and how large the pending commit backlog has grown. The action space is deliberately compact: the agent assigns each task to a high, medium, or low priority queue and selects a coarse-grained container allocation profile on YARN, either single-container or multi-container execution. The Q-network uses two hidden layers of 128 and 64 neurons with ReLU activations and layer normalization, trained with the Adam optimizer at a learning rate of 0.001 and a discount factor of 0.99. An epsilon-greedy strategy decays exploration from 1.0 to 0.05 over 50,000 steps, while a 100,000-entry replay buffer and a target network synchronized every 1,000 gradient updates stabilize learning.

Two additional machine learning components sharpen the scheduler’s judgment. A graph neural network models task dependency structures, treating tasks as nodes and dependencies as edges, and updating node features through weighted aggregation of neighboring information. This captures the spatial relationships among concurrently executing tasks that rule-based schedulers typically ignore. A long short-term memory network then predicts execution-time correlations across scheduling steps, learning from historical traces to anticipate future resource demands. Together, the GNN-LSTM pair feeds dependency and temporal features into the reinforcement learning policy, which converts them into adaptive queue and container decisions.

Cross-ledger synchronization runs on a permissioned consortium blockchain rather than a public network, a choice driven by enterprise requirements for controllable access, low confirmation delay, and auditable transaction traces. The blockchain layer comprises one ordering node, four peer nodes, and two ledger gateway nodes connected to the Spark Streaming workers, with Raft-based consensus avoiding mining delays. When a ledger-update request reaches a synchronization worker, the smart contract registers transaction metadata, ledger identifiers, version numbers, hash digests, priority labels, and timeout thresholds. If the commit condition is satisfied, a zero-knowledge proof module generates a non-interactive consistency proof over hash commitments and range constraints of sensitive accounting fields. Validators confirm the proof and public commitments without ever seeing the underlying values, preserving privacy while guaranteeing atomicity.

The evaluation dataset was assembled from eight medium- and large-scale enterprises that piloted or deployed the system between January and December 2023, yielding 5,000 anonymized task instances. The workload spans four accounting operations: transaction validation accounts for 44 percent of instances, balance aggregation and reconciliation for 30 percent, report generation for 16 percent, and cross-ledger synchronization for 10 percent. Expert-derived priority labels divide tasks into high, medium, and low classes at 29.6, 46.8, and 23.6 percent respectively. High-concurrency scenarios were generated by compressing the arrival intervals of these real records, a replay protocol that stresses queue pressure and ledger contention without inventing new labeled samples.

The benchmark comparisons are striking. A static scheduler achieved 78.2 percent scheduling accuracy, 74.5 percent synchronization accuracy, and a 2.1-second response time. A conventional DRL scheduler without graph modeling or blockchain feedback improved those figures to 85.6 percent, 81.3 percent, and 1.9 seconds. The full DRL-GNN-BC scheduler pushed all three metrics to their best values. Ablation experiments confirmed that each module matters: removing the reinforcement learning unit, the GNN dependency modeling, or the blockchain and zero-knowledge verification degraded response time by 0.28, 0.17, and 0.09 seconds respectively, corresponding to 16.5, 10.0, and 5.3 percent of the full-stack mean. Crucially, a decoupled variant that retained all modules but severed the feedback path from ledger commit status back to the scheduler demonstrated that live synchronization feedback delivers benefits beyond the simple sum of independent components.

Resource costs remain measurable but controlled. The complete framework sustained an average CPU utilization of 74.6 percent and peak memory utilization of 68.9 percent, with the DRL component adding 11.4 percentage points of CPU load and the GNN module a further 6.8 points. The blockchain and zero-knowledge proof layer produced the largest communication overhead, raising network traffic from 12.3 to 18.7 megabytes per second and per-request communication from 0.91 to 1.84 megabytes, though cryptographic verification is triggered only for the 10 percent of tasks involving cross-ledger commits. Throughput held at 60.8 megabytes per second with 96.0 percent stability. Controlled fault-injection tests, including peer-node interruption, network degradation with 300-millisecond delays and 3 percent packet loss, ledger contention, and workload drift, showed recovery times between 12.6 and 18.5 seconds, with the worst degradation occurring under ledger contention, where synchronization accuracy fell 7.6 percentage points and the rollback rate rose to 8.7 percent.

The authors are careful to scope their claims. The 5,000-instance dataset represents a limited enterprise-derived sample, and replayed concurrency does not constitute additional independent observations, so the reported figures characterize the evaluated cluster and replay protocol rather than universal performance. Cross-cluster, cross-platform, and cross-region validation remain future work, as does integration with software-defined networking controllers that could add network-state features to the scheduler’s state vector. Even so, the study offers a concrete demonstration that treating scheduling, resource allocation, and ledger consistency as one optimization problem, rather than loosely coupled subsystems, can yield faster, more auditable, and more resilient financial infrastructure, a design philosophy likely to resonate well beyond the accounting systems where it was first tested.

Subject of Research: Real-time task scheduling and cross-ledger synchronization in enterprise accounting systems using Spark Streaming, YARN, deep reinforcement learning, and blockchain verification

Article Title: Research on cross-ledger synchronization of real-time accounting system based on Spark Streaming and YARN scheduling

Article References: Liu, X., & Bao, W. (2026). Research on cross-ledger synchronization of real-time accounting system based on Spark Streaming and YARN scheduling. Discover Artificial Intelligence, 6(1), Article 1261. https://doi.org/10.1007/s44163-026-02303-y

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02303-y

Keywords: real-time accounting, Spark Streaming, YARN scheduling, deep reinforcement learning, graph neural networks, blockchain, smart contracts, zero-knowledge proofs, cross-ledger synchronization, distributed ledgers, task scheduling, enterprise data systems

The problem the team set out to solve is deceptively simple to state. Intelligent Transportation Systems, the software backbone of future smart cities, need models that can classify street scenes into categories such as city street, highway, residential area or parking lot. Training such models well requires vast amounts of imagery from many geographic locations and weather conditions. But pooling camera footage from thousands of vehicles into a central server is a privacy nightmare, and the vehicles themselves cannot be blindly trusted to report honest results.

Federated learning offers the standard escape hatch: each vehicle trains a model locally on its own data and shares only the resulting parameter updates, never the raw images. Yet the authors identify three stubborn obstacles that have kept this approach from working smoothly in real vehicular networks. First, vehicular data are inherently non-IID, meaning each car sees a statistically different slice of the world depending on where it drives and in what weather. Second, hand-labeling street scenes at fleet scale is prohibitively expensive, so most on-board data goes unused. Third, conventional federated learning simply assumes participants behave honestly, which is a dangerous assumption when a compromised vehicle could submit poisoned updates that corrupt the shared model.

The team’s answer to the label scarcity problem is a confidence-based pseudo-labeling pipeline. On the Mapillary Vistas v2 dataset, which provides only pixel-level semantic segmentation rather than scene labels, the researchers first derive scene labels by mapping dominant pixel classes to the four target categories. Then, in a semi-supervised twist, each vehicle uses the current global model to label its own unlabeled images, keeping only predictions whose maximum softmax probability exceeds a threshold of 0.85. The approach was tested under a harsh Dirichlet non-IID partition with just 10 percent labeled supervision per client. Pseudo-label acceptance rates climbed from 51 percent in the first round to over 80 percent by round 18, and the semi-supervised pipeline reached roughly 77 percent overall accuracy, outperforming a threshold-based labeling baseline by about 20 percentage points.

The cryptographic layer is where the framework gets its name. Each vehicle, after training locally and quantizing its model update from 32-bit floating point to 8-bit integers, generates a zk-SNARK proof using the Groth16 proving system implemented through ZoKrates. A zk-SNARK, or zero-knowledge succinct non-interactive argument of knowledge, lets a prover convince a verifier that a computation was performed correctly without revealing the underlying inputs. In this case, the vehicle proves that a distributed probe of coordinates drawn from its quantized update satisfies a norm-boundedness constraint, essentially certifying that the update’s magnitude falls within acceptable bounds. The proof is a mere 0.90 kilobytes against a model of about 32.36 megabytes, a striking illustration of how succinct these cryptographic artifacts can be.

Verification happens at the edge. Road-Side Units, or RSUs, act as regional aggregators, checking each proof before an update is allowed into the FedAvg averaging process. The choice of Groth16 matters here: its proofs consist of exactly three elliptic curve group elements, and verification reduces to three bilinear pairing checks that run in milliseconds, well within the latency budget of vehicle-to-infrastructure communication. The researchers also computed SHA-256 commitments binding the full transmitted update and its probe, so a proof generated for one update cannot be replayed for another.

The third pillar is accountability. A permissioned Hyperledger Besu blockchain running QBFT consensus maintains a tamper-evident audit trail of every training round, recording only compact metadata: update commitments, proof digests, global model hashes and participant counts. A full round’s on-chain payload for five vehicles amounts to just 736 bytes, with confirmation latency around 200 milliseconds on a four-node testnet. Any participant can later independently verify the integrity of a training round by comparing recomputed hashes against the ledger, providing traceability without exposing model contents.

Scalability measurements revealed both promise and caveats. With 5 to 10 vehicles, proof generation stayed below roughly 25 seconds per round and verification below 11 seconds. At 100 vehicles, those figures rose to 560 and 220 seconds respectively, or about 5.6 seconds of generation and 2.2 seconds of verification per vehicle. The authors are candid that these numbers reflect sequential execution on a single GPU server, with overhead inflated by repeated process initialization and memory contention, and that a distributed deployment with parallel proof generation would look considerably different.

The evaluation on BDD100K, using a weather-driven non-IID partition, compared five aggregation strategies and found FedAvg the most balanced performer, reaching 65.53 percent accuracy and a Macro-F1 of 0.3607 over 50 rounds. Notably, the team tested only benign conditions, so the results do not yet demonstrate resilience against actual poisoning attacks, and the authors acknowledge that an adaptive adversary aware of the fixed probe-selection rule could potentially manipulate unprobed coordinates. Randomized probe selection, empirically calibrated norm thresholds and adversarial evaluations are all flagged as future work.

Even with those limitations, the study marks a meaningful step toward trustworthy collaborative AI on the road. Its components reinforce one another in a virtuous cycle: pseudo-labeling expands each vehicle’s effective training set, verified updates yield a more reliable global model that in turn produces better pseudo-labels, and the blockchain guarantees that the whole process can be audited after the fact. As autonomous driving and connected mobility mature, frameworks like ZK-FL suggest a future where vehicles can learn from each other, prove their honesty, and keep your windshield view to themselves.

Subject of Research: Privacy-preserving federated learning with zero-knowledge proofs for vehicle scene classification in intelligent transportation systems

Article Title: Semi-supervised zero-knowledge federated learning for intelligent transportation systems

Article References: Medvei, M.-M., Modiga, M.-M., Aciobăniței, I., & Ţăpuş, N. (2026). Semi-supervised zero-knowledge federated learning for intelligent transportation systems. Frontiers in Future Transportation, 7, Article 1972925. https://doi.org/10.3389/ffutr.2026.1972925

Image Credits: AI Generated

DOI: 10.3389/ffutr.2026.1972925

Keywords: federated learning, zero-knowledge proofs, zk-SNARKs, intelligent transportation systems, VANETs, semi-supervised learning, pseudo-labeling, blockchain, Hyperledger Besu, privacy-preserving machine learning, Groth16, scene classification

The first problem the researchers identify is deceptively simple to state but hard to solve. In cloud–edge systems, computing nodes advertise their attributes—available memory, processing capacity, current load, and similar characteristics—to schedulers that must decide where each task should run. These attribute values travel across networks and pass through scheduling intermediaries, creating opportunities for eavesdropping, tampering, or forgery. A malicious actor who alters a node’s advertised capacity could divert tasks to compromised hardware, degrade system performance, or open doors to further attacks. Existing scheduling schemes, the authors argue, largely ignore this exposure, treating node metadata as trusted public information even in industrial settings where the stakes include production safety and proprietary process data.

To close this gap, the team developed ZKRRP-SDPA, a scheduling data protection algorithm built on zero-knowledge range proofs adapted for real-valued numbers. Zero-knowledge proofs are a cryptographic technique that lets one party convince another that a statement is true without revealing the underlying data. In this case, a node can prove that its attribute values fall within a legitimate range—say, that its available memory lies between specified bounds—without disclosing the actual values. The researchers enhanced the well-known Bulletproofs construction, originally designed for confidential blockchain transactions, to handle real-valued ranges by linearly mapping continuous attributes into an integer domain using a scaling factor and a zero-point shift, then adding offsets so that all mapped values remain positive and compatible with the proof system.

The mathematics behind the protection is rigorous. Each node generates cryptographic commitments of the form g raised to a secret value multiplied by h raised to a random blinding factor, alongside non-interactive Bulletproofs certificates. Because Bulletproofs possess a simulation-based zero-knowledge property, any adversary observing these commitments learns nothing about the underlying attribute values or even the range endpoints. The authors formally prove three security properties in the paper’s appendices. Confidentiality holds because an attacker cannot distinguish a node’s true attributes from random ones. Integrity holds because the simulation-sound extractability of Bulletproofs makes it computationally infeasible to forge a valid proof for an out-of-range value. Authenticity rests on BLS digital signatures, which are secure against adaptive chosen-message attacks, so no one can impersonate a node or replay its proofs.

Security alone, however, does not move tasks faster. The second half of BWAS-DP, an algorithm the authors call CPAS-TSOA, addresses the scheduling side. Industrial workloads are notoriously bursty: a production surge, a batch of inspection requests, or a machine fault can flood the system with tasks in seconds, while node capacities remain fixed. The researchers attack this problem with congestion prediction powered by LSTM neural networks—recurrent models well suited to learning temporal patterns in traffic. Crucially, they use Bayesian optimization to tune the LSTM’s hyperparameters, a technique that efficiently searches the configuration space rather than relying on manual trial and error. With congestion forecasts in hand, the system dynamically reserves resources before bottlenecks materialize, rather than reacting after queues have already formed.

On top of prediction sits a multi-objective scheduling optimizer that adapts its strategy to both the task and the node. When workloads are light, the scheduler can afford thorough searches for optimal placements; when bursts arrive, it switches to faster, task- and node-adaptive strategies that trade a small amount of optimality for large gains in decision speed. This dual-mode design reflects a practical insight about edge computing: the best scheduling policy depends on conditions, and rigid algorithms tuned for average traffic crumble under extremes. The adaptive layer also improves decision-making efficiency itself, which matters because a scheduler that deliberates too long adds its own latency to every task it dispatches.

The team did not stop at theory. They built a prototype system and ran extensive experiments on both customized and public industrial datasets, comparing BWAS-DP against existing approaches under both anticipated and bursty workload conditions. The results are consistent across metrics. Under anticipated workloads, average latency dropped by 12.3 percent. Under bursty workloads—the scenario that stresses industrial systems most—the latency reduction grew to 18.8 percent, showing that the congestion prediction and adaptive reservation mechanisms pay off precisely when they are needed most. The security metric improved by approximately 11.1 percent, reflecting the cryptographic protections layered into the scheduling pipeline, and decision-making efficiency rose by roughly 7.2 percent, confirming that the adaptive strategies dispatch tasks faster without sacrificing quality.

These gains arrive at a moment when industrial Internet of Things deployments are expanding rapidly, and the research literature reflects that urgency. The paper situates itself within a rich body of prior work: task offloading strategies for railway IoT, parallel scheduling for large-scale cloud–edge collaboration, deep reinforcement learning approaches to resource interleaving, blockchain-assisted secure data sharing, and privacy-preserving storage schemes. What distinguishes BWAS-DP is the combination of cryptographic rigor with workload adaptivity in a single framework. Previous secure scheduling efforts often treated privacy as an add-on that degraded performance, while adaptive schedulers frequently assumed honest participants. By proving that nodes can participate in scheduling without exposing their attributes—and that the system still schedules efficiently under stress—the study challenges the assumption that security and performance are inevitably in tension.

The zero-knowledge component also connects the paper to a broader cryptographic movement. Bulletproofs, introduced at the 2018 IEEE Symposium on Security and Privacy, earned attention for producing short proofs without requiring a trusted setup, making them attractive for decentralized systems. Extending such proofs to real-valued industrial attributes is a meaningful technical step, because physical measurements—temperatures, loads, bandwidth fractions—are continuous quantities that do not map naturally onto the integer arithmetic of standard proofs. The linear mapping and quantization preprocessing described in the paper, together with formal proofs of confidentiality, integrity, and authenticity, offers a template other researchers could apply to any system where continuous node or device characteristics must be verified without disclosure.

For industrial operators, the practical implications are straightforward. Factories considering cloud–edge architectures for latency-sensitive applications—machine vision, real-time control, collaborative robotics—now have evidence that they need not choose between responsive scheduling and protecting node metadata. The reported latency reductions under bursty conditions suggest particular value in environments with unpredictable demand, which describes most real production floors. As edge computing matures and industrial networks grow more distributed, frameworks like BWAS-DP point toward infrastructure where cryptographic guarantees are woven into the scheduling fabric rather than bolted on afterward, and where the system anticipates the next traffic storm instead of merely surviving it.

Subject of Research: Secure and adaptive task scheduling for industrial cloud–edge collaborative computing using zero-knowledge range proofs and LSTM-based congestion prediction

Article Title: Bursty workload-adaptive scheduling with data protections for industrial cloud–edge collaboration

Article References: Zhan, J., Liu, X., Zeng, Z., & Le, X. (2026). Bursty workload-adaptive scheduling with data protections for industrial cloud–edge collaboration. Cluster Computing, 29(12), Article 716. https://doi.org/10.1007/s10586-026-06532-9

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06532-9

Keywords: cloud-edge collaboration, zero-knowledge range proofs, Bulletproofs, bursty workloads, adaptive scheduling, industrial Internet of Things, LSTM networks, Bayesian optimization, data protection, latency reduction, BLS signatures, congestion prediction

The starting point is PRISM, a framework for privacy-preserving set computation over outsourced secret-shared databases. In PRISM, multiple data owners split their records into cryptographic shares and distribute them across servers that never see plaintext data. The servers can execute private set intersection and aggregation queries directly over the shares, and the system offers verifiability guarantees against dishonest servers. But PRISM, like most systems in this family, rests on a quiet assumption: that the data owners themselves behave honestly and follow the protocol. In real consortiums of competing organizations, that assumption is often unrealistic.

The researchers catalog the ways a malicious owner can sabotage a query without ever breaking encryption. An owner might distribute inconsistent secret shares so that different servers hold contradictory versions of the same record. It might outsource values that violate declared attribute-level constraints, such as a numeric field exceeding its permitted range, quietly poisoning every aggregate computed downstream. It might issue unauthorized queries that breach the consortium’s access policies. Or it might interfere at the final stage, selectively approving or refusing to confirm aggregation results, or sending conflicting confirmations to different participants. Each of these attacks preserves data confidentiality while corrupting the correctness of the answers everyone relies on.

To close these gaps, the team built a framework that combines verifiable data outsourcing with query-level governance. The first pillar uses verifiable secret sharing, based on Pedersen commitments, to guarantee that every accepted share is consistent with a single committed plaintext value. When a data owner distributes shares of a value, it also publishes commitments derived from the sharing polynomial’s coefficients. Each server can then check locally, through a simple algebraic relation, whether the share it received matches the committed value. A malicious owner cannot hand out mutually inconsistent shares that still pass this check, and the verification requires no interaction between servers.

Consistency alone, however, is not enough. A malicious owner could share a perfectly consistent value that is simply wrong, such as an out-of-range cost figure or a Boolean indicator that is neither zero nor one. The framework therefore layers zero-knowledge proofs on top of the commitments. For Boolean attributes, the owner proves that the committed value satisfies the equation v times (v minus 1) equals zero, which holds only for zero or one, without revealing which. For numeric attributes, the owner supplies a range proof demonstrating that the committed value lies between zero and a public bound. The same commitment anchors both the share-consistency check and the validity proof, so the two guarantees are cryptographically bound to one another. In experiments, these mechanisms detected every injected malicious input, including tampered shares, invalid Boolean values, and out-of-range numbers, with a false-positive rate of zero.

The second pillar, called Adaptive Query Consensus or AQC, governs the query lifecycle itself. Rather than deploying heavyweight Byzantine fault-tolerant protocols that replicate an entire system state, AQC operates strictly per query under an honest-majority assumption, tolerating fewer than one third malicious owners. Before any query reaches the servers, data owners collectively vote on its admissibility against a consortium-wide policy. Only when at least a two-thirds quorum signs the same query descriptor does the coordinator assemble an authorization certificate, and servers refuse to execute anything without one. For aggregation queries, a second certificate confirms the final result: at least Q owners must sign the same result digest before the answer is accepted.

The safety argument is elegant. Because any two valid quorums must overlap in at least one honest owner, and honest owners sign at most one authorization and one result digest per query instance, two conflicting queries or two conflicting results can never both obtain valid certificates. The coordinator, notably, is not trusted at all; it merely collects votes and cannot determine outcomes without sufficient honest signatures. To handle unstable or malicious coordinators, AQC selects them adaptively based on observed behavior, such as vote-collection latency and certificate-assembly success. Certified timeouts or provable equivocation exclude a coordinator for the remainder of that query phase, and the protocol retries with growing timeout windows, guaranteeing eventual progress when the network stabilizes and enough honest owners participate.

The experimental evaluation is striking for its scale and restraint. Using a Python prototype with datasets drawn from the TPC-H benchmark, the team tested workloads of up to 50 data owners and datasets of up to 5 million records. The cryptographic machinery added only moderate cost: query-computation overhead over the PRISM baseline ranged from roughly 5.3 to 7.9 percent and stayed below 8 percent in every tested configuration. Signature processing contributed just 18 milliseconds to the critical path of a two-phase query, and coordinator selection cost less than a millisecond of local computation. Under congested networks and a 30 percent malicious-owner ratio, full two-phase queries completed in under two seconds. Because verification happens during data outsourcing, its cost amortizes across many subsequent queries.

The adaptive coordinator selection proved its worth in adversarial settings. At a 30 percent malicious ratio, adaptive selection cut completion times by roughly 18.6 to 30.5 percent compared with round-robin and random strategies, required about 0.15 to 0.26 retries per query instead of roughly 0.8, and maintained completion rates between 99 and 99.9 percent. A static-leader strategy, by contrast, collapsed to a 70 percent completion rate when its fixed leader turned malicious. The authors are careful about limits: the framework verifies protocol-level integrity, not the real-world truthfulness of syntactically valid data, and its guarantees hold only under the honest-majority assumption, with the 40 percent malicious setting reported purely as a stress test.

The broader significance lies in reframing where trust must be established in collaborative analytics. Cryptographic privacy has long been treated as sufficient protection for multi-party computation, yet the integrity of shared answers depends on participants who may be strategically misaligned, compromised, or simply unreliable. By binding verifiable secret sharing, zero-knowledge validity proofs, and lightweight query-scoped consensus into a single lifecycle-aware workflow, the researchers show that outsourced multi-owner databases can survive their own owners’ misbehavior without the crushing overhead of full Byzantine replication. The team plans to extend the work toward stronger robustness beyond honest majorities, real cloud deployments with heterogeneous latency, and richer query types and policy constraints, pointing toward consortium analytics that are resilient from data submission to confirmed result.

Subject of Research: Verifiable and robust query processing for multi-owner secret-shared databases under malicious data owners

Article Title: Verifiable and robust query processing for multi-owner secret-shared databases under malicious owners

Article References: Gu, L., Li, J., & Shi, J. (2026). Verifiable and robust query processing for multi-owner secret-shared databases under malicious owners. Cybersecurity, 9(1), Article 231. https://doi.org/10.1186/s42400-026-00674-4

Image Credits: AI Generated

DOI: 10.1186/s42400-026-00674-4

Keywords: privacy-preserving computation, secret sharing, verifiable secret sharing, zero-knowledge proofs, private set intersection, outsourced databases, Byzantine consensus, query governance, malicious data owners, data integrity, multi-party computation, cybersecurity

Federated learning is the architectural backbone of the approach. Instead of pooling patient records in a central server, each hospital trains a local copy of the machine-learning model on its own data and shares only the resulting model parameters, the numerical weights that encode what the model has learned. The central coordinator then merges these parameter updates into a global model that benefits from every institution’s experience without any raw record ever leaving its source. The catch is that naive federated systems can still leak information through the shared updates themselves, and they often ignore how much battery, bandwidth and computation each participating device can afford. Bio-RL-FedOpt attacks both problems directly, organizing its workflow into distinct phases that each carry their own optimization machinery.

The first phase, Secure Data Acquisition, handles the real-time collection of medical data at the network edge. The authors pair lightweight hybrid encryption, which scrambles incoming measurements so they cannot be intercepted in readable form, with an Energy Profiling Layer that continuously estimates the computational cost of gathering and processing each data stream. By tracking energy budgets at this early stage, the system can throttle or reschedule data collection so that the lowest possible computational overhead is incurred before any learning even begins. This matters in practical deployments where wearable sensors, bedside monitors and edge gateways run on limited power, and where a framework that drains device batteries would simply never be adopted by clinical staff.

During Local Model Training, each participating node builds its own predictive model using a hybrid architecture that combines a convolutional neural network with a Transformer. Convolutional layers excel at extracting spatial patterns from structured inputs such as medical images or signal windows, while Transformer blocks capture long-range dependencies across sequences, making the pair well suited to heterogeneous medical datasets that mix imaging, time-series and tabular records. To keep unreliable inputs from corrupting the training process, the framework adds an Adaptive Autoencoder-based Anomaly Detector. The autoencoder learns to reconstruct normal data patterns; when an input cannot be reconstructed accurately, it is flagged as anomalous and handled separately, protecting the shared model from noisy sensors, corrupted transmissions or outright data poisoning attempts.

The heart of the contribution lies in the Federated Optimization stage, which the authors split into two cooperating strategies. First, a reinforcement-learning agent acts as an adaptive controller, dynamically selecting learning parameters such as rates and aggregation settings based on observed training behavior. Rather than relying on hand-tuned hyperparameters that suit one dataset but fail on another, the agent learns which configurations accelerate convergence in the current environment. Second, the global aggregation step is handled by an Energy-Sensitive Tunicate Swarm-based optimizer, a metaheuristic inspired by the swarming and jet-propulsion behavior of tunicate marine organisms. The swarm algorithm searches for a way to merge the participating nodes’ model updates that balances predictive quality against transmission overhead, so that the global model converges quickly while the network exchanges as few bytes as possible.

Privacy protection in the framework operates on two complementary levels. An Energy-Sensitive Differential Privacy method injects carefully calibrated statistical noise into the model updates before they leave each node, mathematically limiting what any observer can infer about any individual patient from the shared parameters. The energy-sensitive component adjusts the noise budget with awareness of the computational and communication costs involved, avoiding the heavy overheads that naive privacy mechanisms can impose. On top of this, a Zero-Knowledge Proof-based blockchain auditing mechanism lets every node in the hospital network verify that the aggregation was performed honestly. Zero-knowledge proofs allow one party to demonstrate that a computation was carried out correctly without revealing the underlying data, and anchoring these proofs on a blockchain creates a tamper-evident audit trail that no single institution can quietly alter.

The authors evaluated Bio-RL-FedOpt against benchmark healthcare datasets, including the MIMIC-IV clinical database demo, measuring four criteria: prediction accuracy, convergence rate, energy consumption and privacy leakage. According to the published abstract, the framework achieved high accuracy, the fastest convergence rate among the compared approaches, low energy consumption during both training and prediction, and almost zero privacy leakage. The dual-phase optimizer appears to be the key differentiator: by letting a reinforcement agent tune the learning dynamics while the tunicate swarm minimizes communication cost, the system reaches a well-trained global model with fewer rounds of parameter exchange than conventional federated averaging schemes would require.

The study arrives amid a rapidly growing literature on energy-aware, privacy-preserving healthcare networks. The paper’s reference list traces a research community converging on similar themes from different directions: blockchain-based clustering for Internet of Things healthcare, federated frameworks for remote patient monitoring and human activity recognition, whale-optimization algorithms for wireless body area networks, and zero-knowledge-proof systems for securing medical records in multi-tenant clouds. What distinguishes the new work is its attempt to unify these strands. Where earlier studies typically optimized one dimension, energy or privacy or accuracy, in isolation, Bio-RL-FedOpt treats them as coupled objectives that must be negotiated simultaneously by a single optimization process.

Important caveats remain. The authors state that all reported data come from simulation reports of the software and tools used in the study, and they note that they are working on implementing the framework with real-world data under appropriate permissions. Simulation results, however encouraging, do not capture every complication of a live hospital network, including intermittent connectivity, legacy equipment, regulatory audits and the unpredictable rhythms of clinical workloads. The framework’s reliance on blockchain auditing also raises questions about scalability and governance that real deployments will need to answer. Still, the modular design, with clearly separated acquisition, training, optimization and auditing stages, means individual components could be tested and improved independently as clinical trials proceed.

If the simulated gains translate into practice, the implications extend well beyond hospitals. The same combination of energy-aware profiling, swarm-based aggregation and verifiable privacy could apply to any federated setting where devices are power-constrained and data is sensitive, from smart grids to industrial sensor networks. For healthcare specifically, the vision is compelling: a network of institutions that jointly learn better diagnostic models than any of them could build alone, while patients’ records never leave the ward, the carbon and battery costs of training stay bounded, and every participant can cryptographically confirm that the collective model was built honestly. The research was published in Neural Computing and Applications, volume 38, article 772, on 3 October 2026, with no external funding declared and no competing interests reported by the authors.

Subject of Research: Energy-aware and privacy-preserving federated learning optimization for healthcare systems

Article Title: Bio-inspired reinforcement federated optimization for energy-aware and privacy-preserving healthcare systems

Article References: Geetha, G., & Ramshankar, N. (2026). Bio-inspired reinforcement federated optimization for energy-aware and privacy-preserving healthcare systems. Neural Computing and Applications, 38(19), Article 772. https://doi.org/10.1007/s00521-026-12377-5

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12377-5

Keywords: federated learning, healthcare AI, reinforcement learning, swarm intelligence, differential privacy, blockchain, zero-knowledge proofs, energy-aware computing, convolutional neural networks, Transformer, anomaly detection, IoT healthcare

Group signatures, first proposed by David Chaum and Eugene van Heyst in 1991, allow a member of a group to sign messages anonymously on behalf of the collective. Anyone holding the group’s public key can verify that a valid group member signed the message, but the signer’s individual identity remains hidden within the anonymity set. To prevent abuse of this anonymity, a designated opener typically holds the power to reveal who signed a given message. The tension at the heart of the technology is obvious: anonymity is valuable, but so is accountability, and traditional designs concentrate enormous tracing power in the hands of the group manager.

The new work builds on a concept pioneered by Diaz and Lehmann at the PKC 2021 conference: group signatures with user-controlled and sequential linkability, or GS-UCSL. In such schemes, signers themselves decide which signatures can be linked, rather than depending on a central authority to perform the linking. Linkability matters in practical settings. A vehicle broadcasting sensor readings to a data lake, for example, may need to prove that a sequence of anonymous reports arrived in the original chronological order, because the ordering itself carries meaning. A fuel-level sequence of 35, 45, 30 and then 40 liters within a short window might signal tampering, whereas 45, 40, 35 and 30 would look normal. Contact tracing systems face similar demands when pseudonymous data spans multiple rotating pseudonyms. The original GS-UCSL scheme, however, suffered three fatal limitations: it was not post-quantum secure, it offered no mechanism to revoke malicious signers, and it omitted traceability entirely.

Dong and Yao’s answer rests on lattice cryptography, the mathematical foundation underlying most post-quantum proposals. Lattice problems such as Module Learning With Errors (MLWE) and Module Short Integer Solution (MSIS) are believed to resist attacks even by large-scale quantum computers running Shor’s algorithm, which would demolish schemes built on integer factorization or discrete logarithms. The authors prove their scheme’s anonymity, traceability, existential unforgeability under chosen-message attack, and non-frameability in the random oracle model, grounding each property in the hardness of these lattice assumptions. Parameter analysis with the Lattice Estimator tool suggests attack costs far beyond practical reach, with the cheapest known attacks requiring on the order of 2 to the power 128 operations or more.

One of the scheme’s most distinctive features is its approach to revocation. Existing revocable group signatures typically rely on revocation lists, whose verification cost grows with the number of revoked members, or on revocation tokens distributed through secure channels, which impose heavy communication overhead. The new design instead uses a revocation polynomial. The group manager encodes each legitimate signer’s revocation secret into a polynomial and publishes it; a signer proves membership in a zero-knowledge proof by evaluating the polynomial at their own secret value. When a malicious member must be expelled, the manager simply resamples the secret and recomputes the polynomial from the remaining values. No revocation list needs to be checked at verification time, no member keys need to be reissued, and the privacy of revoked users is preserved. Verification cost remains constant regardless of how many users have been revoked.

To curb the group manager’s tracing power, the scheme borrows key-oblivious encryption, a primitive introduced by Kohlweiss and Miers and later instantiated on lattices by Ling and colleagues. During registration, the manager rerandomizes each user’s encryption public key. Thanks to the key-oblivious property, no one without the original secret key and the randomness can tell whether a given randomized key is traceable. The manager silently tags some users as traceable and others as non-traceable, and the users themselves cannot detect which category they fall into. When signing, each user encrypts identity information under their own randomized key; only traceable users’ identities can later be recovered by the opener. This splits the group into traceable and non-traceable types without anyone’s awareness, restraining the manager from arbitrarily unmasking every signer. A cuckoo hash table, which guarantees worst-case constant-time insertion, lookup and deletion, serves as the manager’s private registry of traceable users.

Linkability in the scheme comes in three flavors, all under user control. Implicit linkability assigns each signature a pseudonym derived from a scope value and the signer’s secret key: signatures within the same scope are automatically linkable, while those across different scopes remain unlinkable unless the signer proves otherwise. Explicit linkability lets a signer voluntarily claim a set of signatures after the fact. Sequential linkability goes further, allowing a signer to produce a proof that a chain of linked signatures was generated in strict chronological order with no omissions. The mechanism uses lightweight hash chains derived from the signer’s secret key and a state counter, with unique sequential values checked against an append-only bulletin board to defeat replay, reordering and selective disclosure attacks. Recovering the signer’s secret from a pseudonym would require solving the MSIS problem, which is computationally infeasible.

Efficiency was a central design goal. The scheme integrates signature aggregation with non-interactive zero-knowledge proofs of knowledge to enable batch verification of sequentially linked signatures. In experiments implemented in SageMath on a laptop with an Intel Core i7-8650U processor, the authors report that aggregating 500 linked signatures compresses the signature size by roughly 81 percent and cuts verification time by about 83 percent. Setup, key generation, verification and opening all complete within one second; signing and revocation finish within about 3.5 seconds. Joining 500 users simultaneously offline took roughly 982 seconds in total, though a single user’s join takes only a few seconds. Communication overhead grows only mildly and linearly with group size, and verification time stays constant regardless of the number of revoked users, which the authors highlight as critical for large-scale deployments such as vehicular networks and blockchain-based data sharing.

The formal security analysis proceeds through sequences of games. Anonymity reduces to the hiding property of an underlying lattice commitment scheme by Baum and colleagues plus the indistinguishability of a verifiable encryption scheme by Lyubashevsky and Neven. Traceability, unforgeability and non-frameability share a unified proof structure: any adversary who forges a signature can be used, via the general forking lemma, to extract a solution to the MSIS problem, with the three properties distinguished by the adversary’s goals and oracle access rather than by structurally different proofs. The authors also validated the protocol with the automated verification tool Scyther, which confirmed the security of the message flows among the group manager, signer and verifier. They acknowledge that the security proofs rely on the random oracle model, consistent with all state-of-the-art lattice-based group signatures of this scope, and note that achieving such comprehensive functionality in the standard model remains an open problem. Hash functions are instantiated with the NIST-standardized, post-quantum-secure SHAKE-256.

The researchers acknowledge remaining limitations and outline future work. Currently, the group manager must recompute the revocation polynomial after every revocation operation, which costs time, and the manager must store each legitimate signer’s revocation secret, so storage demand grows with the signer base. The team plans to optimize polynomial updates and to seek smaller secret keys and lighter management overhead. Even so, the authors conclude that LCGS-UCSL achieves comprehensive functionality with competitive efficiency, marking the first post-quantum-secure group signature scheme to combine user-controlled sequential linkability, lightweight polynomial revocation, oblivious traceability classification and batch verification. For privacy-critical sequential scenarios ranging from intelligent transportation to contact tracing and time-series data authentication, the scheme offers a blueprint for staying anonymous, staying accountable, and staying secure against the quantum computers of the future.

Subject of Research: A post-quantum lattice-based group signature scheme with user-controlled and sequential linkability and efficient revocation

Article Title: An efficient lattice-based conditional privacy-preserving group signature with user-controlled and sequential linkability

Article References: Dong, S., & Yao, Y. (2026). An efficient lattice-based conditional privacy-preserving group signature with user-controlled and sequential linkability. Cybersecurity, 9(1), Article 210. https://doi.org/10.1186/s42400-026-00613-3

Image Credits: AI Generated

DOI: 10.1186/s42400-026-00613-3

Keywords: group signatures, lattice cryptography, post-quantum security, user-controlled linkability, sequential linkability, revocation, zero-knowledge proofs, key-oblivious encryption, cuckoo hashing, anonymity, traceability, batch verification