Tracking the Invisible: Privacy-Preserving Contact Tracing to Control the Spread of a Virus
This addresses privacy concerns in contact tracing for public health, offering a scalable solution that balances privacy and efficiency, though it builds incrementally on existing methods.
The paper tackles the problem of controlling virus spread through contact tracing while preserving privacy, proposing a private set intersection technique that protects location privacy and hides patient identities, with efficiency and scalability demonstrated via simulations.
Today, tracking and controlling the spread of a virus is a crucial need for almost all countries. Doing this early would save millions of lives and help countries keep a stable economy. The easiest way to control the spread of a virus is to immediately inform the individuals who recently had close contact with the diagnosed patients. However, to achieve this, a centralized authority (e.g., a health authority) needs detailed location information from both healthy individuals and diagnosed patients. Thus, such an approach, although beneficial to control the spread of a virus, results in serious privacy concerns, and hence privacy-preserving solutions are required to solve this problem. Previous works on this topic either (i) compromise privacy (especially privacy of diagnosed patients) to have better efficiency or (ii) provide unscalable solutions. In this work, we propose a technique based on private set intersection between physical contact histories of individuals (that are recorded using smart phones) and a centralized database (run by a health authority) that keeps the identities of the positive diagnosed patients for the disease. Proposed solution protects the location privacy of both healthy individuals and diagnosed patients and it guarantees that the identities of the diagnosed patients remain hidden from other individuals. Notably, proposed scheme allows individuals to receive warning messages indicating their previous contacts with a positive diagnosed patient. Such warning messages will help them realize the risk and isolate themselves from other people. We make sure that the warning messages are only observed by the corresponding individuals and not by the health authority. We also implement the proposed scheme and show its efficiency and scalability via simulations.