PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
Justin Chan, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph, Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet, Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro

TL;DR
This paper proposes privacy-preserving mobile contact tracing protocols that avoid third-party involvement, aiming to balance public health needs with individual privacy and security concerns during COVID-19.
Contribution
It introduces a third-party free contact tracing approach and discusses privacy and inferential risks, advancing privacy-preserving solutions for pandemic response.
Findings
Supports privacy preservation without trusted third parties
Addresses inferential privacy risks in contact tracing
Facilitates multi-sector collaboration on privacy issues
Abstract
The global health threat from COVID-19 has been controlled in a number of instances by large-scale testing and contact tracing efforts. We created this document to suggest three functionalities on how we might best harness computing technologies to supporting the goals of public health organizations in minimizing morbidity and mortality associated with the spread of COVID-19, while protecting the civil liberties of individuals. In particular, this work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party. We also explicitly consider the inferential risks involved in any contract tracing system, where any alert to a user could itself give rise to de-anonymizing information. More generally, we hope to participate in bringing together colleagues in industry,…
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Taxonomy
TopicsCOVID-19 Digital Contact Tracing · Privacy-Preserving Technologies in Data · Privacy, Security, and Data Protection
