Paper 2021/1125
Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks
Luise Mehner, Saskia Nuñez von Voigt, and Florian Tschorsch
Abstract
Differential privacy is a concept to quantify the disclosure of private information that is controlled by the privacy parameter~$\varepsilon$. However, an intuitive interpretation of $\varepsilon$ is needed to explain the privacy loss to data engineers and data subjects. In this paper, we conduct a worst-case study of differential privacy risks. We generalize an existing model and reduce complexity to provide more understandable statements on the privacy loss. To this end, we analyze the impact of parameters and introduce the notion of a global privacy risk and global privacy leak.
Note: Accepted on International Workshop on Privacy Engineering – IWPE'21. Co-located with 6th IEEE European Symposium on Security and Privacy September 7, 2021, Vienna online
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. Minor revision. 2021 International Workshop on Privacy Engineering – IWPE'21. Co-located with 6th IEEE European Symposium on Security and Privacy September 7, 2021, Vienna online
- Keywords
- privacy riskdifferential privacy
- Contact author(s)
- saskia nunezvonvoigt @ tu-berlin de
- History
- 2021-09-06: received
- Short URL
- https://ia.cr/2021/1125
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2021/1125,
author = {Luise Mehner and Saskia Nuñez von Voigt and Florian Tschorsch},
title = {Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks},
howpublished = {Cryptology {ePrint} Archive, Paper 2021/1125},
year = {2021},
url = {https://eprint.iacr.org/2021/1125}
}
