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Poirot: private contact summary aggregation: poster abstract

Published: 16 November 2020 Publication History

Abstract

Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensitive and can be used to track individuals. In this work, we design Poirot, a system to collect aggregate statistics about physical interactions in a privacy-preserving manner. We show a preliminary evaluation of our system that demonstrates the scalability of our approach even while maintaining strong privacy guarantees.

References

[1]
Kimon Drakopoulos. 2020. The Logic Around Contact Tracing Apps Is All Wrong. https://www.wired.com/story/opinion-the-logic-around-contact-tracing-apps-is-all-wrong/.
[2]
Gabriel Kaptchuk, Daniel G. Goldstein, Eszter Hargittai, Jake M. Hofman, and Elissa M. Redmiles. 2020. How good is good enough for COVID19 apps? https://arxiv.org/pdf/2005.04343.pdf.

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      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 16 November 2020

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      Author Tags

      1. COVID19
      2. bluetooth tracing
      3. differential privacy
      4. multi-party computation
      5. private data aggregation

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      • NSF SaTC

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      Overall Acceptance Rate 174 of 867 submissions, 20%

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