Paper 2020/792

Trace-$\Sigma$: a privacy-preserving contact tracing app

Jean-François Biasse, Sriram Chellappan, Sherzod Kariev, Noyem Khan, Lynette Menezes, Efe Seyitoglu, Charurut Somboonwit, and Attila Yavuz


We present a privacy-preserving protocol to anonymously collect information about a social graph. The typical application of our protocol is Bluetooth-enabled ``contact-tracing apps'' which record information about proximity between users to infer the risk of propagation of COVID-19 among them. The main contribution of this work is to enable a central server to construct an anonymous graph of interactions between users. This graph gives the central authority insight on the propagation of the virus, and allows it to run predictive models on it while protecting the privacy of users. The main technical tool we use is an accumulator scheme due to Camenisch and Lysyanskaya to keep track of the credentials of users, and prove accumulated credentials in Zero-Knowledge.

Available format(s)
Publication info
Preprint. MINOR revision.
Exposure notificationcontact tracingZero-Knowledge proofssigma-protocolsAccumulatorsSignatures of KnowledgeCOVID-19.
Contact author(s)
biasse @ usf edu
2020-08-04: revised
2020-06-27: received
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      author = {Jean-François Biasse and Sriram Chellappan and Sherzod Kariev and Noyem Khan and Lynette Menezes and Efe Seyitoglu and Charurut Somboonwit and Attila Yavuz},
      title = {Trace-$\Sigma$: a privacy-preserving contact tracing app},
      howpublished = {Cryptology ePrint Archive, Paper 2020/792},
      year = {2020},
      note = {\url{}},
      url = {}
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