New Research Links f-Differential Privacy to Quantitative Information Flow, Proposes General Composition Theorems

Global: New Research Links f-Differential Privacy to Quantitative Information Flow, Proposes General Composition Theorems

A study released on December 23, 2025, presents a novel theoretical bridge between f-differential privacy (fDP) and the channel model of Quantitative Information Flow (QIF). The paper, authored by Natasha Fernandes, Annabelle McIver, and Parastoo Sadeghi, aims to enhance the analytical tools available for complex privacy designs.

Understanding f‑Differential Privacy

f‑Differential privacy is a recent formulation that refines traditional differential privacy by framing privacy loss in terms of statistical hypothesis testing. This perspective allows for more precise predictions of privacy leakage, especially when evaluating mechanisms such as the Gaussian mechanism commonly used in data‑sharing applications.

Equivalence to Quantitative Information Flow

The authors demonstrate that the statistical‑testing foundation of fDP is mathematically equivalent to the channel model employed in QIF. By constructing a Galois connection between two partially ordered sets—one representing privacy guarantees and the other representing information‑flow channels—the paper establishes a formal correspondence that unifies the two domains.

General Composition Theorems

Leveraging the established equivalence, the researchers derive new composition theorems for fDP. These theorems provide systematic methods for aggregating privacy guarantees across multiple mechanisms, thereby supporting more accurate analysis of composite systems where privacy loss accumulates.

Implications for Privacy Engineering

The composition results are expected to improve the design and verification of privacy‑preserving algorithms. By offering tighter bounds on cumulative privacy loss, developers can better balance utility and protection when deploying complex pipelines that incorporate several privacy‑enhancing components.

Positioned at the intersection of cryptography, security, and information theory, the work contributes to ongoing scholarly discussions in both the cs.CR and cs.IT communities. Its classification under the arXiv categories “Cryptography and Security” and “Information Theory” reflects the interdisciplinary relevance of the findings.

The paper is accessible through arXiv with DOI 10.48550/arXiv.2512.21358, and the full text is available for public download. Researchers and practitioners interested in advanced privacy analysis are encouraged to consult the preprint for detailed proofs and methodological explanations.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.