Casual Users and Rational Choices within Differential Privacy
Narges Ashena, Oana Inel, Badrie L. Persaud, Abraham Bernstein
IEEE Symposium on Security and Privacy 2024 · Day 1 · Continental Ballroom 6
This presentation, delivered by Narges Ashena, delves into the critical challenge of making **Differential Privacy (DP)** comprehensible and actionable for everyday users. Differential Privacy is a robust privacy framework designed to allow insights to be gained from datasets while rigorously protecting the privacy of individual data points. A core component of DP is the **Epsilon (ε)** parameter, which acts as a knob to calibrate the trade-off between privacy protection and the utility (accuracy) of the query results. Setting Epsilon is notoriously difficult, even for experts, and the talk highlights the significant gap between expert recommendations (typically ε < 1) and the wide range of values observed in real-world applications (0.1 to nearly 50).
AI review
This empirical study tackles a critical problem for Differential Privacy: making Epsilon understandable to casual users. The research rigorously demonstrates how interactive visualizations can guide users towards more privacy-preserving choices, despite their inherent sensitivity to perceived data accuracy.