Privacy-Preserving Adaptive Trajectory Storage on Blockchain for COVID-19 Contact Tracing

Abstract

Privacy preservation in various contact tracing approaches for the COVID-19 or SARS-CoV-2 virus is challenging, as such applications tend to reveal users’ points of interest (POIs) and other sensitive data shared together with their location information. This paper proposes COVID-19 eavesdropping resistant tracing (COVERT)-Blockchain, a novel distributed-ledger-based platform to facilitate contact tracing without invading users’ privacy. COVERT-Blockchain enables infected users to share only their anonymized location traces on the Blockchain with a sliding window of the previous 15 days, thereby avoiding constant location information sharing with third party users. To further reduce the chances of revealing the corresponding users’ trajectories, in COVERT-Blockchain we employ an adaptive logging mechanism to store trajectory data for contact tracing only if the users stayed in a location where there is significant presence of other humans around them for a relatively long duration of time. This ensures anonymity where the trajectory is generated differently each time for each user, and such infrequent and random trajectory generation enables us to generate unidentifiable trajectories for each user and thus preserve their privacy. COVERT-Blockchain is evaluated for scalability and robustness in relation to overhead and delays in storing and retrieving data from the Blockchain. Results show it to efficiently achieve contact tracing without any breaches of privacy.