Provable Privacy Guarantee for Individual Identities and Locations in Large-Scale Contact Tracing

TL;DR

This paper introduces PREVENT, a scalable and privacy-preserving contact tracing system that ensures individual location privacy while providing real-time, large-scale trajectory analysis using secret sharing and private space partitioning.

Contribution

It presents a novel, efficient contact tracing system that guarantees privacy without revealing plain text locations, applicable to large datasets and diverse location collection methods.

Findings

Negligible performance overhead compared to traditional methods

Effective privacy preservation for large-scale contact tracing

Real-time query support on datasets with millions of locations

Abstract

The task of infectious disease contact tracing is crucial yet challenging, especially when meeting strict privacy requirements. Previous attempts in this area have had limitations in terms of applicable scenarios and efficiency. Our paper proposes a highly scalable, practical contact tracing system called PREVENT that can work with a variety of location collection methods to gain a comprehensive overview of a person's trajectory while ensuring the privacy of individuals being tracked, without revealing their plain text locations to any party, including servers. Our system is very efficient and can provide real-time query services for large-scale datasets with millions of locations. This is made possible by a newly designed secret-sharing based architecture that is tightly integrated into unique private space partitioning trees. Notably, our experimental results on both real and synthetic datasets demonstrate that our system introduces negligible performance overhead compared to traditional contact tracing methods. PREVENT could be a game-changer in the fight against infectious diseases and set a new standard for privacy-preserving location tracking.