Asymmetric Differential Privacy (preprint)

Differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. This paper focuses on the limitation that DP inevitably causes two-sided error, which is not desirable for epidemic analysis such as how many COVID-19 infected individuals visited location A. For example, consider publishing misinformation that many infected people did not visit location A, which may lead to miss decision-making that expands the epidemic. To fix this issue, we propose a relaxation of DP, called asymmetric differential privacy (ADP). We show that ADP can provide reasonable privacy protection while achieving one-sided error. Finally, we conduct experiments to evaluate the utility of proposed mechanisms for epidemic analysis using a real-world dataset, which shows the practicality of our mechanisms.

PANDA: Policy-aware Location Privacy for Epidemic Surveillance (preprint)

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.