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
Creative Commons Attribution
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},
      note = {\url{https://eprint.iacr.org/2021/1125}},
      url = {https://eprint.iacr.org/2021/1125}
}
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