| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | May 15, 2013 |
| Latest Amendment Date: | November 21, 2017 |
| Award Number: | 1254169 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Dan Cosley
dcosley@nsf.gov (703)292-8832 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | June 1, 2013 |
| End Date: | May 31, 2018 (Estimated) |
| Total Intended Award Amount: | $541,993.00 |
| Total Awarded Amount to Date: | $541,993.00 |
| Funds Obligated to Date: |
FY 2014 = $105,695.00 FY 2015 = $108,002.00 FY 2016 = $110,378.00 FY 2017 = $112,825.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1200 E CALIFORNIA BLVD PASADENA CA US 91125-0001 (626)395-6219 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
CA US 91125-0001 |
| 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: |
01001415DB NSF RESEARCH & RELATED ACTIVIT 01001516DB NSF RESEARCH & RELATED ACTIVIT 01001617DB NSF RESEARCH & RELATED ACTIVIT 01001718DB 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
This project takes a new approach to problems involving sensitive data, by focusing on rigorous mathematical modeling and characterization of the value of private information. By focusing on quantifying the loss incurred by affected individuals when their information is used -- and quantifying the attendant benefits of such use -- the approaches advanced by this work enable concrete reasoning about the relative risks and rewards of a wide variety of potential computations on sensitive data.
Specifically, this work has four main technical thrusts. The first is the development of new models and definitions, enabling privacy considerations to be incorporated into agent utility functions. The second is analysis of the feasibility and costs of eliciting sensitive information, in light of these models. The third focus is on enabling more sophisticated computations in settings where individuals value their privacy. Finally, more complex settings incorporate the interests of additional actors.
One of the goals of this project is not only to develop a science of the value of private information, but to build bridges between computer science and economics that will enable such work. Further, the models and algorithms developed by this project could inform future regulation regarding the use, exchange, and monetization of sensitive data. The project supports and is supported by a wide variety of educational goals, including significant research involvement of students at a range of stages, development of a course series with a substantial research component, and assessment of a pedagogical technique created to facilitate meaningful engagement with research literature.
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.
This project has focused on the study of incentive issues related to data privacy, including questions such as: who might share data with whom and why, how could people be compensated for the use of their private data, and how to gather and use data coming from people who might have an incentive to misrepresent or hide their data. To this end, we have developed new models of incentives in information-sharing in social networks, reflecting a trade-off between the risks and benefits of sharing information.
This project has also considered issues that stem from making the formal mathematical tools used in the study of privacy more applicable real-world problems. This grant has supported work on a paper written for economists who work with personal data, in order to introduce them to the computer science privacy literature. One surprising result that has come from this work highlights the risks in blindly applying privacy-preserving technologies without considering the broader context; we see that if the parties involved might change their behavior as a result of an increased use of such technologies, their effect could have the opposite of what was intended. We have also studied the problem of maximizing the level of privacy that can be offered to participants in a dataset, subject to accuracy constraints on the computations that will be done with the data.
Last Modified: 09/06/2018
Modified by: Adam C Wierman
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