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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK, THE
Doing Business As Name:Columbia University
PD/PI:
  • Rachel Cummings
  • (404) 894-4819
  • rachelc@gatech.edu
Award Date:09/19/2021
Estimated Total Award Amount: $ 175,000
Funds Obligated to Date: $ 40,667
  • FY 2019=$40,667
Start Date:07/15/2021
End Date:04/30/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CRII: SaTC: Data Privacy for Strategic Agents
Federal Award ID Number:2147657
DUNS ID:049179401
Parent DUNS ID:049179401
Program:Secure &Trustworthy Cyberspace
Program Officer:
  • James Joshi
  • (703) 292-8950
  • jjoshi@nsf.gov

Awardee Location

Street:2960 Broadway
City:NEW YORK
State:NY
ZIP:10027-6902
County:New York
Country:US
Awardee Cong. District:10

Primary Place of Performance

Organization Name:Columbia University
Street:2960 Broadway
City:New York
State:NY
ZIP:10027-0420
County:New York
Country:US
Cong. District:13

Abstract at Time of Award

This project lays the groundwork for understanding how existing tools for privacy-preserving data analysis interact with strategic and human aspects of practical privacy guarantees. When strategic individuals have privacy concerns about the use of their data, they may modify their behavior to ensure less, or perhaps more favorable, information is revealed. The project's novelties are an interdisciplinary approach, which combines tools from algorithm design, machine learning, and economics. The broader significance and importance of this work is to provide a critical step for society's ability to collect useful data and to interpret data via existing algorithms. As more personal data are collected, stored, and used in algorithmic decision making, these results are useful in the legal and policy landscape of personal data management. This work has two main technical thrusts. First, this project studies how privacy technologies can be designed and deployed to manage privacy concerns of strategic individuals. This yields insight into the design of optimal privacy technologies for strategic individuals in practical application areas. Second, this project develops data analysis techniques for settings where data are generated by privacy-aware individuals. This yields tools for the design and analysis of algorithms to efficiently learn and optimize from a strategic individual's data. This project also includes a significant educational and outreach component, including curriculum development, mentorship of students, and workshop organization. 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.

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