Ayday, ErmanYoo, Y.Halimi, A.2022-02-092022-02-092021-08-01978-145038450-6http://hdl.handle.net/11693/77160Conference Name: BCB '21: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health InformaticsDate of Conference: 1- 4 August 2021We propose a novel privacy-preserving COVID-19 risk assessment algorithm that can make a fundamental contribution to the development of the next generation resilient public health and health care systems. The proposed algorithm, ShareTrace, uses a hyperlocal interaction graph to capture direct and indirect physical interactions among users. Combining user-reported symptoms that are propagated through the hyperlocal interaction graph via a novel message passing algorithm, ShareTrace is able to pick up early warning signals based on the combination of interactions with others and symptoms. The proposed algorithm is inspired by the belief propagation algorithm and iterative decoding of low-density parity-check codes over factor graphs. Our evaluation on synthetic data shows the efficiency and efficacy of the proposed solution.EnglishCOVID-19Digital contact tracingBelief-propagation algorithmPrivacyHyperlocal interaction graphConference Paper