DIMY: Enabling privacy-preserving contact tracing. (June 2022)

• Record Type: Journal Article
• Authors: Ahmed, Nadeem; Michelin, Regio A.; Xue, Wanli; Putra, Guntur Dharma; Ruj, Sushmita; Kanhere, Salil S.; Jha, Sanjay
• Is Part Of: Journal of network and computer applications. Volume 202(2022)
• Publication Date: 2022-06
• Subjects: COVID-19 -- Contact tracing -- Bloom filter -- Blockchain -- Privacy -- Security; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2022.103356
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: The infection rate of COVID-19 and the rapid mutation ability of the virus has forced governments and health authorities to adopt lockdowns, increased testing, and contact tracing to reduce the virus's spread. Digital contact tracing has become a supplement to the traditional manual contact tracing process. However, although several digital contact tracing apps are proposed and deployed, these have not been widely adopted due to apprehensions surrounding privacy and security. In this paper, we present a blockchain-based privacy-preserving contact tracing protocol, "Did I Meet You" (DIMY). The protocol provides full-lifecycle data privacy protection on the devices as well as the back-end servers to address most of the privacy concerns associated with existing protocols. We have employed Bloom filters to provide efficient privacy-preserving storage and have used the Diffie–Hellman key exchange for secret sharing among the participants. We show that DIMY provides resilience against many well-known attacks while introducing negligible overheads. DIMY's footprint on the storage space of clients' devices and back-end servers is also significantly lower than other similar state-of-the-art apps. Highlights: We focus on the privacy and security of digital contact tracing data. Recent analysis of existing apps has revealed several security and privacy issues. We provide safeguards against several type of attacks with an extenisve threat model. Our method reduces the storage requirements both on mobile devices and the backend. Performance evaluations have proved the efficacy of the proposed protocol.