Cryptology ePrint Archive: Report 2021/1125

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

Luise Mehner and 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.

Category / Keywords: applications / privacy risk, differential privacy

Original Publication (with minor differences): 2021 International Workshop on Privacy Engineering – IWPE'21. Co-located with 6th IEEE European Symposium on Security and Privacy September 7, 2021, Vienna online

Date: received 3 Sep 2021, last revised 3 Sep 2021

Contact author: saskia nunezvonvoigt at tu-berlin de

Available format(s): PDF | BibTeX Citation

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

Version: 20210906:074414 (All versions of this report)

Short URL: ia.cr/2021/1125


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