Project Grant 2517121
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program project grant, awarded on June 1, 2024 for $174,975.00, supports research to develop innovative systems software for confidential computing in distributed cloud services. The key objectives are: (1) extending Transport Layer Security (TLS) protocols to enable secure authentication and authorization for computing nodes in a mixed trusted/untrusted environment, and (2) exploring storage...
- The National Science Foundation (NSF) awarded a $1.2M Project Grant to the University of Virginia under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to enhance the security and privacy of community cyberinfrastructures (CI) for collaborative research. This 3-year project focuses on developing advanced techniques to secure data management and computation within CI systems, including centralized computing, distributed computing, and data computation. The...
- The University of California, San Diego received a three-year, $500,000 Project Grant award from the National Science Foundation Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support the CICI: UCSS: ENHANCING INTEGRITY AND CONFIDENTIALITY FOR SECURE DISTRIBUTED DATA SHARING project from July 2021 through June 2024. The project aims to advance the development and use of research cyberinfrastructure to enable...
- This $900,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program aims to fortify and enrich confidential computing environments (CCEs) for cloud-based applications. The project, awarded to Yale University, focuses on designing better software, studying security weaknesses, and creating new methods to protect sensitive data in cloud computing platforms from malicious attacks. The research will explore techniques like mixing...
- This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $600,000 to Florida State University (FSU) to develop a security-focused framework to protect collaborative scientific computing in Machine Learning as a Service (MLaaS) environments. The key products and services to be delivered include: Robust model protection techniques to hinder reverse engineering of machine learning models while preserving...
- This $599,059 National Science Foundation project grant supports the development of secure multi-party computation programming technology at Rensselaer Polytechnic Institute. The goal is to advance compiler frameworks and optimization techniques that allow non-cryptography experts to write efficient privacy-preserving algorithms for applications like machine learning and data analytics. Key deliverables include an intermediate representation for secure computation and novel intra- and...
- This Project Grant award of $164,999 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is for a project titled "CRII: SATC: TOWARDS COST-EFFICIENT PRIVATE COMPUTATION FOR CLOUD DATA SCIENCE". The objective is to develop techniques that reduce the cost of practical Secure Multiparty Computation (MPC) for data science in the cloud. This will enable more cost-effective collaborative data science on private datasets, leading to...
- This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $374,291 to Brown University to develop novel tools and techniques for secure multi-party computation (MPC), with a focus on expanding the use cases of private set intersection (PSI). The project aims to bridge the gap between standard PSI and more enriched PSI functionalities, enabling large-scale, privacy-preserving data analytics across...
- 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...
- This $209,368 federal Project Grant award from the National Science Foundation's Integrative Activities program (CFDA 47.083) aims to develop trusted, low-overhead tools that enable computation directly on encrypted data. The goal is to accelerate the creation of new capabilities that allow confidential data to be shared with untrusted parties who can extract insights without accessing the unencrypted data. This would increase public trust in modern AI tools and enable data-powered, socially...
CICI: UCSS: CONFIDENTIAL COMPUTING IN REPRODUCIBLE COLLABORATIVE WORKFLOWS -DATA-INTENSIVE SCIENTIFIC RESEARCH PROJECTS OFTEN INVOLVE MULTIPLE COLLABORATIVE PARTIES. SOME PARTIES MAY DEMAND CONFIDENTIAL PROCESSING OF THEIR SENSITIVE ASSETS TO PROTECT INTELLECTUAL PROPERTY, EMBARGO DATA (OR ALGORITHM) SHARING BEFORE PUBLISHING A PAPER, CONFORM TO LEGAL REQUIREMENTS, OR AVOID THE RESPONSIBILITY FOR RELEASING SENSITIVE DATA. HOWEVER, INTEGRATING CONFIDENTIAL COMPUTING INTO SCIENTIFIC WORKFLOWS RAISES SIGNIFICANT CHALLENGES. (1) MOST SCIENCE DOMAIN DEVELOPERS FIND IT CHALLENGING TO LEARN SPECIFIC CONFIDENTIAL COMPUTING FRAMEWORKS AND SECURE THEIR CODE TO PROTECT FROM SIDE-CHANNEL ATTACKS. (2) THE INTERPLAY BETWEEN THE PRIVATE COMPONENTS AND OTHER COMPONENTS IN A COLLABORATIVE WORKFLOW MAY ENABLE NEW ATTACKS AND SIDE CHANNELS FOR ADVERSARIES TO EXPLORE. THE PROPOSED PROJECT AIMS TO ADDRESS THESE CHALLENGES WITH A SCIENTIST-FRIENDLY DEVELOPMENT FRAMEWORK FOR CONFIDENTIAL COMPUTING AND A HOLISTIC ATTACK STUDY AND MITIGATION FRAMEWORK FOR COLLABORATIVE WORKFLOWS. THE SUCCESS OF THIS PROJECT WILL ENABLE DOMAIN SCIENTIST DEVELOPERS TO ADOPT THE BEST CONFIDENTIAL COMPUTING PRACTICES EASILY AND USE PUBLICLY AVAILABLE RESOURCES WITHOUT THE CONCERN OF CONFIDENTIALITY AND PRIVACY BREACH, BOOSTING THE IDEA OF OPEN, COLLABORATIVE SCIENCE. SPECIFICALLY, THE PROPOSED RESEARCH FOCUSES ON THE SCIENTIST-ORIENTED TRUSTED-EXECUTION-ENVIRONMENT (TEE) BASED DEVELOPMENT AND STUDIES ITS INTEGRATION WITH COLLABORATIVE SCIENTIFIC WORKFLOWS. (1) THE PROJECT EXPLORES DIFFERENT PROTECTION AND USABILITY SOLUTIONS FOR DOMAIN SCIENTISTS AND ALLOWS THEM TO TRADEOFF BETWEEN THEIR RESEARCH GOALS AND SECURITY AND PRIVACY CONCERNS. (2) IT DEVELOPS AN EFFICIENT AND TRANSPARENT TEE ACCESS-PATTERN PROTECTION FRAMEWORK THAT UNIQUELY COMBINES THE BEST PRACTICES IN DATA-INTENSIVE COMPUTING AND FRAMEWORK-BASED MITIGATION METHODS. (3) IT TAKES A HOLISTIC APPROACH TO STUDY NEW SECURITY AND PRIVACY THREATS AROUND CONFIDENTIAL COMPONENTS IN A COLLABORATIVE WORKFLOW, COVERING STAGES INCLUDING TASK EXECUTION, LOGGING, PROVENANCE ANALYSIS, AND REPRODUCIBILITY VERIFICATION. THE SOLUTIONS WILL INTEGRATE TECHNIQUES LIKE TEE, BLOCKCHAIN, AND DIFFERENTIAL PRIVACY. (4) IT IS SCIENCE-DRIVEN, MOTIVATED, AND VALIDATED BY COLLABORATIVE RESEARCH PROJECTS IN BIOMEDICAL SEQUENCE PROCESSING, IMAGE-BASED REMOTE DIAGNOSIS, AND HEALTHCARE DATA ANALYTICS. THIS PROJECT WILL GENERATE OPEN-SOURCE TOOLKITS AND DEMONSTRATION SYSTEMS. IT ALSO INCLUDES SEVERAL EDUCATIONAL AND OUTREACH INITIATIVES TO ENHANCE CYBERSECURITY AND DATA SCIENCE PROGRAMS, ATTRACT UNDERREPRESENTED STUDENTS, HELP LOCAL HIGH SCHOOL CS EDUCATION, AND STRENGTHEN INDUSTRIAL COLLABORATIONS. 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 NOT PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 6/20/25 | ||
| Not listed | $432.5k | 4/7/25 |