Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks

Luise Mehner; Saskia Nuñez von Voigt; Florian Tschorsch

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~ $ε$ . However, an intuitive interpretation of $ε$ 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 risk differential 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}
}