NSF Backs $5 Million Effort to Let Scientists Share Sensitive Data Without Ever Exposing It

Medical researchers at two different universities may need to compare patient records to accelerate drug discovery. Cybersecurity analysts at separate organizations may want to pool intelligence about attacks on their networks to better defend against adversaries. Yet privacy concerns, institutional safeguards and legal regulations routinely prevent them from sharing and collaborating on sensitive information, including personally identifiable information and intellectual property. A new $5 million award from the U.S. National Science Foundation’s Office of Advanced Cyberinfrastructure aims to dissolve that barrier. Over the next five years, researchers at San Diego State University, in partnership with Arizona State University and the University of Utah, will build and operate a national community resource for computing on and sharing private data without ever exposing the underlying records.

The infrastructure, called Prototype Architecture for Research Advances using Privacy Enhancing Technologies, or PARAPET, is designed to serve a wide range of fields that depend on data analysis, including healthcare, internet security, science, finance and the social sciences. Its central promise is that researchers can analyze medical records, financial information, census statistics and other regulated datasets collaboratively while the data itself remains protected both at rest and during computation. The project also addresses one of the most pressing challenges of the current era: training artificial intelligence models on distributed, sensitive data without exposing the private information those models learn from.

Robert Beverly, director of SDSU’s Cybersecurity Center for Research and Education and professor of computer science, framed the problem in blunt terms. There are many cases, he noted, where privacy considerations preclude researchers from working on data together, and that stands as a genuine impediment to science. Researchers are entrusted with sensitive data, so it must be protected, and moving these technologies into production, he argued, promises to advance the entire field. The grant, awarded under NSF award number 2537035, positions the three-institution team to test whether cutting-edge cryptographic and systems techniques can survive contact with real research workloads at national scale.

The technical core of the challenge is well understood in the cryptography community. Today, data can be encrypted for transmission across networks and for storage on disks, and those protections are mature and widely deployed. Performing computations on encrypted data, however, remains difficult. PARAPET aims to make that process practical. The team plans to equip the platform with specialized software and hardware tools that allow one user to send encrypted data that another user can operate on without decrypting it, ensuring the data is never revealed to anyone other than the original owner. In principle, a hospital could hand an encrypted dataset to an outside analytics collaborator, who could run statistical models against it while remaining mathematically unable to see a single patient record.

These same techniques carry an additional dividend: resilience against future quantum computing attacks, which pose a heightened threat to today’s cyber systems because sufficiently powerful quantum machines could break many widely used public-key encryption schemes. By building computation-on-encrypted-data capabilities into the platform from the start, PARAPET gives the research community a national-scale testbed for exploring tools that could remain secure even in a post-quantum world. The project also takes a defensive posture toward compromise. The entire system is designed so that encrypted data is locked away and accessible only to approved users on secured networks, with the explicit goal that even a successful breach yields little of value.

Joann Chen, assistant professor of computer science at SDSU, described that philosophy directly: the long-term goal is that even if the infrastructure were compromised and an attacker obtained a copy of encrypted data, they could not do much with it. That assumption-that breach is eventual and design must assume it-marks a shift from perimeter-focused security toward architectures where the data itself carries its protection. It also reflects a growing consensus in the security research community that anonymization alone is not enough. Beverly pointed out that many attackers can conduct re-identification attacks, reverse engineering supposedly de-identified datasets to link records back to specific individuals, a technique demonstrated repeatedly in studies of released medical and census data.

To counter those risks, PARAPET will lean on a family of privacy-enhancing technologies that have matured rapidly in recent years. Differential privacy adds carefully calibrated statistical noise so that aggregate results reveal population-level patterns without exposing any individual contribution. Homomorphic encryption allows arithmetic to be performed directly on ciphertext. Federated learning lets machine learning models be trained across many devices or institutions without any raw data leaving its source. With secure computing hardware combining these approaches, PARAPET seeks to provide a vastly more secure option for supporting high-performance computing needs than either raw data transfer or simple anonymization.

The rise of artificial intelligence makes this work especially timely. As AI spreads into virtually every discipline, protecting data while models are built has become essential, and PARAPET is designed to safeguard users’ data during the fine-tuning of central AI models that can be used nationwide. One approach the team will test is federated: users train a local model on their own device and share only the model updates with PARAPET, which then aggregates the information from those updates to improve its larger model without ever accessing the raw data. Chen offered an intuitive explanation, noting that any AI model can be seen as a giant math function, and the system essentially takes the outputs from that function and averages them across the different local models. Unlike systems that require organizations to transfer raw data to a central service, PARAPET will test methods that let participating institutions retain far greater control over their sensitive research data.

Each partner institution brings a distinct mandate. At the University of Utah, Manish Parashar, chief AI officer and executive director of the Scientific Computing and Imaging Institute, will help manage PARAPET’s hardware and ensure the infrastructure is ready for deployment at national scale. Parashar observed that data-driven and AI-enabled research is revolutionizing science, but that progress in critical fields such as cybersecurity and public health is hampered by the inability to obtain, create, compute on and share regulated data. PARAPET, he said, takes an important step toward solving that problem by leveraging advances in privacy-enhancing technologies, and Utah looks forward to contributing expertise and leadership in designing and operating the compliant, regulated environments the platform needs to safely accelerate research with sensitive data.

At Arizona State University, Carolyn Ellis, an expert on regulatory and policy compliance and director of ASU’s Research Technology Office, will ensure PARAPET meets rapidly evolving regulatory requirements and will lead efforts to recruit users from across the nation to test the platform once a prototype is ready. Ellis, a co-principal investigator on the project, said privacy-enhancing technologies have enormous potential to change how research is conducted with sensitive or regulated datasets, but that the team must first understand how these new capabilities fit within institutional policies, research workflows and the privacy expectations established through data-sharing agreements. Through ASU’s leadership of the national Regulated Research Community of Practice, the project gains a vital institutional perspective that bridges the gap between new technical capabilities and the practical realities of protecting sensitive research data. At SDSU, chief technology research officer Mike Farley, who oversees the on-campus data center where PARAPET’s servers will be housed, emphasized that the project is assembling specialized hardware, software, networking, security and data workflows into a prototype researchers can actually use, helping clarify what it takes to move these technologies from the lab into practical research cyberinfrastructure. The effort builds on SDSU’s growing portfolio of interdisciplinary cyber initiatives and its development of new cybersecurity and data privacy curricula under the Cybersecurity Center for Research and Education, positioning the university as an emerging hub for privacy-preserving computation research.

Subject of Research: A national privacy-enhancing cyberinfrastructure for secure sharing and computation on sensitive research data

Article Title: NSF awards $5M across three institutions to enhance data protection for users nationwide

Article References: NSF awards $5M across three institutions to enhance data protection for users nationwide. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: privacy-enhancing technologies, cybersecurity, NSF grant, PARAPET, data sharing, homomorphic encryption, federated learning, differential privacy, AI model training, research cyberinfrastructure, data protection, San Diego State University