Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks
| Title | Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Mehner, Luise, Voigt, Saskia Nuñez von, Tschorsch, Florian |
| Conference Name | 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS PW) |
| Date Published | Sept. 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-1012-0 |
| Keywords | Complexity theory, composability, Differential privacy, Human Behavior, privacy, privacy risk, pubcrawl, resilience, Resiliency, Scalability, ε |
| Abstract | Differential privacy is a concept to quantity the disclosure of private information that is controlled by the privacy parameter e. However, an intuitive interpretation of e 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. |
| URL | https://ieeexplore.ieee.org/document/9583708 |
| DOI | 10.1109/EuroSPW54576.2021.00041 |
| Citation Key | mehner_towards_2021 |