PANDA: Policy-aware Location Privacy for Epidemic Surveillance

TL;DR

This paper introduces a privacy-preserving epidemic surveillance system that uses policy-based location privacy, enabling effective monitoring and contact tracing without compromising individual location privacy.

Contribution

It formalizes a policy graph approach based on differential privacy, balancing epidemic surveillance needs with location privacy protection.

Findings

Effective location monitoring and contact tracing are achievable with privacy preservation.

The system demonstrates a practical privacy-utility trade-off.

Attendees can explore the system's usability and privacy features.

Abstract

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's location data to eliminate the outbreak. However, it raises privacy concerns and may open the doors to more invasive forms of surveillance in the name of public health. It also brings a challenge for privacy protection techniques: how can we leverage people's mobile data to help combat the pandemic without scarifying our location privacy. We demonstrate that we can have the best of the two worlds by implementing policy-based location privacy for epidemic surveillance. Specifically, we formalize the privacy policy using graphs in light of differential privacy, called policy graph. Our system has three primary functions for epidemic surveillance: location monitoring, epidemic analysis, and contact tracing. We provide an interactive tool allowing the attendees to explore and examine the usability of our system: (1) the utility of location monitor and disease transmission model estimation, (2) the procedure of contact tracing in our systems, and (3) the privacy-utility trade-offs w.r.t. different policy graphs. The attendees can find that it is possible to have the full functionality of epidemic surveillance while preserving location privacy.