Shows that (epsilon,delta)-differential privacy bounds an adversary's posterior probability, posterior-to-prior ratio, and posterior-to-prior difference with high probability.
SoK: Usability Studies in Differential Privacy
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abstract
Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in the implementation and communication of DP. This paper presents a comprehensive systematization of existing research studies around the usability of DP, synthesizing insights from studies on both the practical use of DP tools and strategies for conveying DP parameters that determine privacy protection levels, such as epsilon($\varepsilon$). By reviewing and analyzing these studies, we identify core usability challenges, best practices, and critical gaps in current DP tools that affect adoption across diverse user groups, including developers, data analysts, and non-technical stakeholders. Our analysis highlights actionable insights and pathways for future research that emphasizes user-centered design and clear communication, fostering the development of more accessible DP tools that meet practical needs and support broader adoption.
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Interpreting Differential Privacy in Terms of Disclosure Risk
Shows that (epsilon,delta)-differential privacy bounds an adversary's posterior probability, posterior-to-prior ratio, and posterior-to-prior difference with high probability.