Contact tracing has been considered as an effective measure to limit the transmission of infectious disease such as COVID-19. Trajectory-based contact tracing compares the trajectories of users with the patients, and allows the tracing of both direct contacts and indirect contacts. Although trajectory data is widely considered as sensitive and personal data, there is limited research on how to securely compare trajectories of users and patients to conduct contact tracing with excellent accuracy, high efficiency, and strong privacy guarantee. Traditional Secure Multiparty Computation (MPC) techniques suffer from prohibitive running time, which prevents their adoption in large cities with millions of users. In this work, we propose a technical framework called ContactGuard to achieve accurate, efficient, and privacy-preserving trajectory-based contact tracing. It improves the efficiency of the MPC-based baseline by selecting only a small subset of locations of users to compare against the locations of the patients, with the assist of Geo-Indistinguishability, a differential privacy notion for Location-based services (LBS) systems. Extensive experiments demonstrate that ContactGuard runs up to 2.6$\times$ faster than the MPC baseline, with no sacrifice in terms of the accuracy of contact tracing.

翻译：接触追踪已被视为限制新冠病毒等传染病传播的有效措施。基于轨迹的接触追踪通过比较用户与患者的行动轨迹，可实现直接接触与间接接触的双重追踪。尽管轨迹数据被广泛视为敏感个人信息，目前关于如何安全比较用户与患者轨迹以实现兼具高精度、高效率与强隐私保障的接触追踪的研究仍十分有限。传统的安全多方计算（MPC）技术因运行时间过长而难以应用于拥有数百万用户的大型城市。本研究提出名为ContactGuard的技术框架，旨在实现精准、高效且保护隐私的轨迹接触追踪。该框架在基于地理不可区分性（一种面向位置服务（LBS）系统的差分隐私概念）的支持下，仅选择用户位置的少量子集与患者位置进行比对，从而提升MPC基线的效率。大量实验表明，ContactGuard的运行速度较MPC基线提升高达2.6倍，且未牺牲接触追踪的准确性。