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Long Live Randomization: On Privacy-preserving Contact Tracing in Pandemic

Published: 09 November 2020 Publication History

Abstract

Caused by coronavirus SARS-CoV-2, the COVID-19 disease spreads particularly through direct contact between people. Health authorities face the challenge of identifying and isolating infection chains to prevent the pandemic from spreading further. To improve the efficiency and effectiveness of manual contact tracing, many countries have recently introduced digital contact tracing apps running on smartphones of users for helping to identify contacts between individual users. These apps are usually based on beaconing pseudonymous identifiers over a proximity communication protocol like Bluetooth LE. The identification of potentially critical contacts is then performed by comparing the identifiers emitted by persons reported as infected and the identifiers observed by other users of the system and issuing appropriate warnings to them in case a matching identifier is found. However, by beaconing identifiers into their proximity, individual users potentially become traceable by entities that systematically collect observations in various places. To preserve privacy of users and be compliant to various privacy regulations many proposed systems use ephemeral, pseudo-random identifiers that are more difficult to link together.
In this paper, we briefly analyze and discuss privacy properties of a selected number of proposed contact tracing solutions and the impact of the applied randomization approaches. We also discuss the pros and cons of these tracing schemes.

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Cited By

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  • (2022)Modeling Trust in COVID-19 Contact-Tracing Apps Using the Human-Computer Trust Scale: Online Survey StudyJMIR Human Factors10.2196/339519:2(e33951)Online publication date: 13-Jun-2022
  • (2022)Speculative VulnerabilityProceedings of the ACM on Human-Computer Interaction10.1145/35555866:CSCW2(1-27)Online publication date: 11-Nov-2022

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cover image ACM Conferences
MTD'20: Proceedings of the 7th ACM Workshop on Moving Target Defense
November 2020
96 pages
ISBN:9781450380850
DOI:10.1145/3411496
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Publication History

Published: 09 November 2020

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Author Tags

  1. bluetooth proximity detection
  2. contact tracing
  3. encounter token
  4. pandemic
  5. privacy-preserving
  6. pseudonymous identifier
  7. randomization

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  • Research-article

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  • Deutsche Forschungsgemeinschaft (DFG)

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CCS '20
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Overall Acceptance Rate 40 of 92 submissions, 43%

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Cited By

View all
  • (2022)Modeling Trust in COVID-19 Contact-Tracing Apps Using the Human-Computer Trust Scale: Online Survey StudyJMIR Human Factors10.2196/339519:2(e33951)Online publication date: 13-Jun-2022
  • (2022)Speculative VulnerabilityProceedings of the ACM on Human-Computer Interaction10.1145/35555866:CSCW2(1-27)Online publication date: 11-Nov-2022

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