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Cross hashing: anonymizing encounters in decentralised contact tracing protocols

  • Junade Ali
    ,
  • Vladimir Dyo
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Open access

Abstract

During the COVID-19 (SARS-CoV-2) epidemic, Contact Tracing emerged as an essential tool for managing the epidemic. App-based solutions have emerged for Contact Tracing, including a protocol designed by Apple and Google (influenced by an open-source protocol known as DP3T). This protocol contains two well-documented de-anonymisation attacks. Firstly that when someone is marked as having tested positive and their keys are made public, they can be tracked over a large geographic area for 24 hours at a time. Secondly, whilst the app requires a minimum exposure duration to register a contact, there is no cryptographic guarantee for this property. This means an adversary can scan Bluetooth networks and retrospectively find who is infected. We propose a novel ”cross hashing” approach to cryptographically guarantee minimum exposure durations. We further mitigate the 24-hour data exposure of infected individuals and reduce computational time for identifying if a user has been exposed using k-Anonymous buckets of hashes and Private Set Intersection. We empirically demonstrate that this modified protocol can offer like-for-like efficacy to the existing protocol.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Article number

9333939

Pages from-to (Number of pages)

Pages 181-185 (5 pages)

Publication milestones

  • Published - 02/02/2021

Publication status

Published - 02/02/2021

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

Publication series

  • Publication series name: International Conference on Information Networking
    ISSN (Print): 1976-7684
    Volume: 2021-January

ISBN (Electronic)

9781728191003

Publication IDs

  • handle.net: 10547/624753
  • Scopus: 85100796032

Host publication title

35th International Conference on Information Networking, ICOIN 2021

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