Project Grant 2452708
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $279,959 to Carnegie Mellon University to advance the frontiers of differential privacy algorithms for private learning and synthetic data generation. The 5-year research project aims to develop a theoretical framework to better capture practical privacy scenarios, design practical privacy-preserving algorithms, and create auditing...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award of $377,003 to the Massachusetts Institute of Technology (MIT) aims to develop a suite of privacy-preserving web services. The key products to be delivered include: Private machine-learning inference, allowing clients to evaluate server-side machine-learning models on their private data without revealing the input data. Private search, enabling clients to search server-side document...
- This National Science Foundation (NSF) Project Grant award under the Technology, Innovation, and Partnerships (CFDA 47.084) program provides $1,297,636 to The Trustees of the University of Pennsylvania to develop the Trusted Integration Data Exchange System. This secure platform will enable government agencies, healthcare organizations, and research institutions to perform complex data analysis on jointly held sensitive datasets while maintaining compliance with data sharing policies and...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to develop a database architecture that integrates privacy regulations and compliance processes, enhances federated machine learning with decentralized data management functions, and automates privacy-model configuration in artificial intelligence workflows. The $249,998 award to Arizona State University will be used to address the...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant of $174,995, awarded to the San Diego State University Foundation, will support research to develop methods for applying differential privacy to provenance graphs. Provenance tracks the origin, usage, and modifications of data, which is critical for verifying data integrity and enabling compliance with privacy regulations in sectors like healthcare, finance, and government. However,...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program provides $640,348 to Carnegie Mellon University from February 2021 through September 2023 to support research into rethinking access pattern privacy from theory to practice. The award will fund collaborative work between Carnegie Mellon University and Cornell University to explore alternative notions of privacy and their relationships to established concepts like differential...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $374,291 to Brown University to expand the use cases of secure multi-party computation (MPC), a technique that allows multiple parties to perform computations on combined private data while preserving privacy. The key objectives of this 5-year project are to: 1) Develop new tools to enhance private set intersection (PSI) capabilities and address...
- This $175,000 two-year Project Grant from the National Science Foundation's Division of Computer and Network Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of novel local differential privacy techniques to significantly improve the privacy-utility tradeoff in multi-attribute data analysis. The Rochester Institute of Technology will develop techniques exploiting correlation in multi-attribute data and correlated random...
- This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides USD 174,991 to Portland State University to develop novel approaches to protect databases against dependency-based attacks that could allow attackers to infer sensitive information. The project aims to formulate inference detection and protection as optimization problems to solve them efficiently, and develop an incremental and collaborative approach to...
- This three-year $599,999 Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), will support research into novel methods for computing aggregate statistics on streaming data in a privacy-preserving manner. Specifically, the University of California, Los Angeles will explore efficient algorithms to privately compute telemetry data from user devices sending...
PDASP TRACK 1: ENABLING A PRIVACY-PRESERVING DATA LIFE CYCLE WITH LIGHTWEIGHT SECURE COMPUTATION -MODERN SOCIETY DEPENDS ON ANALYZING MASSIVE AMOUNTS OF PERSONAL INFORMATION TO IMPROVE HEALTHCARE, ENHANCE NATIONAL SECURITY, AND DRIVE ECONOMIC GROWTH. HOWEVER, CURRENT PRACTICES FOR STORING AND SHARING SENSITIVE DATA HAVE LED TO MAJOR DATA BREACHES EXPOSING MILLIONS OF PEOPLE'S PERSONAL INFORMATION, INCLUDING MEDICAL RECORDS, FINANCIAL DETAILS, AND GOVERNMENT SECRETS, UNDERMINING PUBLIC TRUST AND THREATENING NATIONAL SECURITY. THIS PROJECT ADDRESSES THIS CRITICAL CHALLENGE BY DEVELOPING NEW COMPUTER SYSTEMS THAT ALLOW ORGANIZATIONS TO GAIN INSIGHTS FROM LARGE DATASETS WHILE KEEPING INDIVIDUAL INFORMATION COMPLETELY PRIVATE. THIS PROJECT WILL BRING PRIVACY PROTECTIONS TO EACH OF THE THREE STEPS OF THE DATA-MANAGEMENT LIFECYCLE: DATA COLLECTION, DATA PROCESSING, AND DATA RETRIEVAL. BY PROTECTING PRIVACY DURING DATA COLLECTION, ANALYSIS, AND RETRIEVAL, THIS RESEARCH SERVES THE NATIONAL INTEREST BY ENABLING CONTINUED TECHNOLOGICAL ADVANCEMENT WHILE SAFEGUARDING CITIZEN PRIVACY, SUPPORTING ECONOMIC COMPETITIVENESS IN DATA-DRIVEN INDUSTRIES, AND STRENGTHENING CYBERSECURITY INFRASTRUCTURE. THIS PROJECT INVESTIGATES THREE FUNDAMENTAL RESEARCH AREAS TO ADVANCE PRIVACY-PRESERVING DATA SYSTEMS. FIRST, THE RESEARCH TEAM WILL DEVELOP NEW PROTOCOLS FOR PRIVACY-PRESERVING DATA COLLECTION THAT ENABLE SERVERS TO COMPUTE AGGREGATE STATISTICS OVER CLIENT DATA WITHOUT ACCESSING INDIVIDUAL RECORDS, WITH EMPHASIS ON REDUCING COMPUTATIONAL COSTS AND EXPANDING THE CLASS OF COMPUTABLE FUNCTIONS COMPARED TO EXISTING SYSTEMS. SECOND, THE PROJECT WILL DESIGN PRIVACY-PRESERVING MACHINE LEARNING ALGORITHMS FOR TRAINING RECOMMENDER SYSTEMS, CLUSTERING ALGORITHMS, AND DECISION TREES THAT OPERATE ON ENCRYPTED DATA WHILE MAINTAINING MODEL ACCURACY. THE THIRD AND LAST COMPONENT OF THE PROJECT WILL BE TO DEVELOP NEW TECHNIQUES THAT LET CLIENTS PRIVATELY QUERY SERVER-SIDE DATASETS. IN THIS THRUST, THE PROJECT WILL DEVELOP A RELATIONAL DATABASE THAT SUPPORTS PRIVATE QUERIES. A KEY DESIGN COMPONENT WILL BE NEW DATA STRUCTURES, OPTIMIZED TO WORK WITH CRYPTOGRAPHIC PRIVACY-PROTECTING PROTOCOLS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $13.8k | 8/21/25 |