Composition Theorems for f-Differential Privacy
Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi
TL;DR
This paper establishes a theoretical connection between f-differential privacy and information flow, leading to new composition theorems that enhance the analysis of complex privacy mechanisms.
Contribution
It demonstrates the equivalence of fDP to the channel model of Quantitative Information Flow, enabling novel composition theorems for privacy analysis.
Findings
fDP is equivalent to the channel model of Quantitative Information Flow
New composition theorems for fDP improve privacy analysis
Supports analysis of complex privacy mechanisms
Abstract
"f differential privacy" (fDP) is a recent definition for privacy privacy which can offer improved predictions of "privacy loss". It has been used to analyse specific privacy mechanisms, such as the popular Gaussian mechanism. In this paper we show how fDP's foundation in statistical hypothesis testing implies equivalence to the channel model of Quantitative Information Flow. We demonstrate this equivalence by a Galois connection between two partially ordered sets. This equivalence enables novel general composition theorems for fDP, supporting improved analysis for complex privacy designs.
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Adversarial Robustness in Machine Learning · Internet Traffic Analysis and Secure E-voting