  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.

Cryptography and Security