Asymmetric Differential Privacy
This work addresses a specific problem in privacy-preserving data publishing for epidemic analysis, offering an incremental improvement over standard differential privacy.
The paper tackles the limitation of differential privacy causing two-sided error in epidemic analysis by proposing asymmetric differential privacy (ADP), which achieves one-sided error while providing reasonable privacy protection, as demonstrated through experiments on a real-world dataset.
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.