Cross Hashing: Anonymizing encounters in Decentralised Contact Tracing Protocols
This addresses privacy vulnerabilities in contact tracing apps during epidemics like COVID-19, offering incremental improvements to existing protocols.
The paper tackled de-anonymization attacks in decentralized contact tracing protocols, such as Apple and Google's, by proposing a cross-hashing method that cryptographically guarantees minimum exposure durations and mitigates 24-hour data exposure, empirically showing like-for-like efficacy.
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.