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dc.contributor.authorCanetti, Ranen_US
dc.contributor.authorKalai, Y.T.en_US
dc.contributor.authorLysyanskaya, A.en_US
dc.contributor.authorRivest, R.L.en_US
dc.contributor.authorShamir, A.en_US
dc.contributor.authorShen, E.en_US
dc.contributor.authorTrachtenberg, A.en_US
dc.contributor.authorVaria, Mayanken_US
dc.contributor.authorWeitzner, D.J.en_US
dc.date.accessioned2021-10-27T13:53:46Z
dc.date.available2021-10-27T13:53:46Z
dc.date.issued2020
dc.identifier.citationR. Canetti, Y.T. Kalai, A. Lysyanskaya, R.L. Rivest, A. Shamir, E. Shen, A. Trachtenberg, M. Varia, D.J. Weitzner. 2020. "Privacy-Preserving Automated Exposure Notification.." IACR Cryptol. ePrint Arch., Volume 2020, pp. 863 - 863.
dc.identifier.urihttps://hdl.handle.net/2144/43224
dc.description.abstractContact tracing is an essential component of public health efforts to slow the spread of COVID-19 and other infectious diseases. Automating parts of the contact tracing process has the potential to significantly increase its scalability and efficacy, but also raises an array of privacy concerns, including the risk of unwanted identification of infected individuals and clandestine collection of privacy-invasive data about the population at large. In this paper, we focus on automating the exposure notification part of contact tracing, which notifies people who have been in close proximity to infected people of their potential exposure to the virus. This work is among the first to focus on the privacy aspects of automated exposure notification. We introduce two privacy-preserving exposure notification schemes based on proximity detection. Both systems are decentralized - no central entity has access to sensitive data. The first scheme is simple and highly efficient, and provides strong privacy for non-diagnosed individuals and some privacy for diagnosed individuals. The second scheme provides enhanced privacy guarantees for diagnosed individuals, at some cost to efficiency. We provide formal definitions for automated exposure notification and its security, and we prove the security of our constructions with respect to these definitions.en_US
dc.format.extentp. 863en_US
dc.language.isoen_US
dc.relation.ispartofIACR Cryptol. ePrint Arch.
dc.titlePrivacy-preserving automated exposure notificationen_US
dc.typeArticleen_US
dc.description.versionFirst author draften_US
pubs.elements-sourcedblpen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Administrationen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.organisational-groupBoston University, Faculty of Computing & Data Sciencesen_US
dc.identifier.mycv567246


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