Award Abstract # 2207214
Collaborative Proposal: SaTC: Frontiers: Center for Distributed Confidential Computing (CDCC)

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: DUKE UNIVERSITY
Initial Amendment Date: July 26, 2022
Latest Amendment Date: August 11, 2024
Award Number: 2207214
Award Instrument: Continuing Grant
Program Manager: Xiaogang (Cliff) Wang
xiawang@nsf.gov
 (703)292-2812
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2022
End Date: September 30, 2027 (Estimated)
Total Intended Award Amount: $1,500,000.00
Total Awarded Amount to Date: $900,000.00
Funds Obligated to Date: FY 2022 = $300,000.00
FY 2023 = $300,000.00

FY 2024 = $300,000.00
History of Investigator:
  • Michael Reiter (Principal Investigator)
    michael.reiter@duke.edu
  • Fan Zhang (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Duke University
2200 W MAIN ST
DURHAM
NC  US  27705-4640
(919)684-3030
Sponsor Congressional District: 04
Primary Place of Performance: Duke University
NC  US  27708-0129
Primary Place of Performance
Congressional District:
04
Unique Entity Identifier (UEI): TP7EK8DZV6N5
Parent UEI:
NSF Program(s): Secure &Trustworthy Cyberspace
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
01002526DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT

01002627DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 025Z, 8087
Program Element Code(s): 806000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Advances in AI and big data analytics rely on data sharing, which can be impeded by privacy concerns. Most challenging in privacy protection is protection of data-in-use, since even encrypted data needs to be decrypted before it can be utilized, thereby exposing data content to unauthorized parties. A practical and scalable solution to the challenge will transform computing, enabling unprecedented capabilities such as confidential outsourcing, trusted computing services, and confidential or privacy-preserving collaboration. In quest of such a holy grail of data protection, this frontier project establishes multi-institution and multi-disciplinary Center for Distributed Confidential Computing (CDCC) to create a research, education, knowledge transfer and workforce development environment that enables scalable, practical, verifiable and usable data-in-use protection based upon Trusted Execution Environments (TEE) on cloud and edge systems.

CDCC focuses on four building block thrusts fundamental to distributed confidential computing (DCC), regardless of specific TEE hardware, including assurance of TEE code, assurance of TEE nodes, assurance of a TEE workflow and assurance for the stakeholder. The first thrust leads to an open ecosystem for TEE code certification, not relying on any trusted party but on a trustworthy application store whose certification operations are public, accountable and verifiable. The second thrust aims to develop novel dynamic data-use policy models and enforcement mechanisms for scalable trust management and data control on the TEE nodes running certified code. The third thrust focuses on ensuring protection of the computational workflow built on TEE nodes and the last thrust studies the stakeholder's preference and expectations to guide the design of DCC technologies and ensure their usability. On top of these building blocks, the center explores various transformative applications (e.g., confidential distributed AI supports for healthcare) to be enabled. CDCC also has a number of efforts for outreach (development of a massive open online course, industry collaboration, etc.) and for participation by all students.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Zhou, Lulu and Liu, Zeyu and Zhang, Fan and Reiter, Michael K "CrudiTEE: A Stick-And-Carrot Approach to Building Trustworthy Cryptocurrency Wallets with TEEs" , v.316 , 2024 https://doi.org/10.4230/LIPIcs.AFT.2024.16 Citation Details
Jain, P and Reed, A and Reiter, MK "Near-optimal constrained padding for object retrievals with dependencies" , 2024 Citation Details
Reed, A. C. and Reiter, M. K. "Optimally hiding object sizes with constrained padding" IEEE Computer Security Foundations Symposium , 2023 https://doi.org/10.1109/CSF57540.2023.00004 Citation Details

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