Reproduce “Balancing the privacy-utility trade-off: How to draw reliable conclusions from private data” on OpenResearch

About this paper

Raphaël de Fondeville

An interpretable framework for managing the privacy-utility trade-off in differentially private data was developed, leveraging f-Differential Privacy and Bayesian analysis. This framework quantifies disclosure risk using metrics like relative disclosure risk and categorizes mechanisms based on their vulnerability to "blatant disclosure," offering practical guidance for parameter selection.

Key insights

• DP mechanisms can be categorized into three types based on their f-DP trade-off function properties and relative disclosure risk: "catastrophic failure" (blatant disclosure), "graceful failure" (unbounded ρ but no blatant disclosure), and "no failure" (bounded ρ).
• The abstract privacy-loss parameters of DP can be translated into interpretable statistical measures, such as the relative disclosure risk (ρ ≤ e^ϵ for ϵ-DP) and the statistical power of an attack, making DP guarantees more accessible.
• DP-induced noise can be treated as a quantifiable source of uncertainty, allowing for the assessment of its impact on the statistical power of legitimate hypothesis tests, demonstrating that strong privacy can often be achieved without severe utility loss for many practical applications.

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