
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | December 31, 2015 |
Latest Amendment Date: | April 2, 2020 |
Award Number: | 1553437 |
Award Instrument: | Continuing Grant |
Program Manager: |
Sara Kiesler
skiesler@nsf.gov (703)292-8643 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | February 1, 2016 |
End Date: | January 31, 2022 (Estimated) |
Total Intended Award Amount: | $520,957.00 |
Total Awarded Amount to Date: | $520,957.00 |
Funds Obligated to Date: |
FY 2017 = $100,759.00 FY 2018 = $101,188.00 FY 2019 = $111,719.00 FY 2020 = $107,360.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
1 NASSAU HALL PRINCETON NJ US 08544-2001 (609)258-3090 |
Sponsor Congressional District: |
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Primary Place of Performance: |
87 Prospect Avenue, 2nd Floor Princeton NJ US 08544-2020 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Secure &Trustworthy Cyberspace |
Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT 01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Social media systems have transformed our societal communications, including news discovery, recommendations, societal interactions, E-commerce, as well as political and governance activities. However, the rising popularity of social media systems has brought concerns about security and privacy to the forefront. This project aims to design trustworthy social systems by building on the discipline of network science. First, the project is developing techniques for analysis of social media data that protect against risks to individual privacy; new research is needed since existing approaches are unable to provide rigorous privacy guarantees. Second, the project is developing new approaches to mitigate the threat of "fake accounts" in social systems, in spite of attempts by the creators of those accounts to elude detection. Both deployed and academic approaches remain vulnerable to strategic adversaries, motivating the development of novel defense mechanisms based on network science. The findings and new designs from this research will directly impact the security and privacy of a broad class of social network users.
The private network analytics thrust builds on the ideas of differential privacy, ensuring sufficient uncertainty in results to hide individual relationships. The project introduces dependent differential privacy, which protects against disclosure of information associated with an individual, as well as mutual information privacy, an entropy-based measure. The Sybil mitigation thrust is based on the idea of adversarial machine learning: the creators of fake accounts are presumed to adapt their mechanisms to changing detection approaches. This work exploits new features, such as temporal dynamics of the network, to address this problem. Finally, the project aims to integrate the research with an educational initiative for developing pedagogical approaches and content for trustworthy social systems.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The major goal of this project was to explore the synergy between trustworthy systems and network/data science techniques. Our approach exploited the structural and temporal properties of networked systems and real-world data to enhance security and privacy.
Our research led to the development of algorithms and systems that (1) secured networked systems against various attacks, (2) enhanced the privacy of user communications, (3) enabled privacy-preserving data analytics, and (4) mitigated attacks against machine learning techniques.
This project led to substantial real-world impact, including (1) deployment of new defense mechanisms at Let's Encrypt, the world's largest certificate authority, that led to the secure issuance of over 1 billion TLS certificates, (2) enhancing the performance of the Tor network, (3) integration of our privacy mechanisms in Google's TensorFlow privacy, (4) blacklisting of malicious accounts at social networks such as Twitter, (5) integration of developed systems by NEC labs, and uncovering privacy and security vulnerabilities in smart TV devices like Roku.
Our research received multiple awards, such as the 2020, 2021, and 2022 Runner Up, Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies. Our research has been broadly disseminated in the research community, and led to training opportunities for both graduate students and undergraduate students.
Last Modified: 08/31/2022
Modified by: Prateek Mittal
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