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Creators/Authors contains: "Lentz, Matthew"

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  1. ; ; ; ; (, ACM)
    Free, publicly-accessible full text available May 14, 2026
  2. ; ; ; ; ; ; (, ACM)
    Full Text Available 
  3. ; ; ; ; ; ; ; (, Proceedings of The 45th IEEE Symposium on Security and Privacy (IEEE SP))
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; (, SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems)
    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. 
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