# Asymmetric Differential Privacy

arXiv: https://arxiv.org/abs/2103.00996 · HTML: https://mlsanity.com/paper/2103.00996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Adversarially Robust Bloom Filters: Privacy, Reductions, and Open Problems](https://mlsanity.com/paper/2501.15751/) (2025)

## Abstract (arXiv)

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.