Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy

2026-05-25

AI Summary

Existing local differential privacy (LDP) protocols lack a systematic and principled approach for comparing privacy strength across diverse adversary models, as reliance solely on the privacy parameter ε or utility metrics proves insufficient for comprehensive evaluation. This work introduces, for the first time, quantitative information flow (QIF) theory and Blackwell’s refinement order into LDP analysis, modeling protocols as probabilistic channels to formally compare seven prominent frequency estimation mechanisms—GRR, BLH, OLH, SUE, OUE, and THE. The proposed framework reveals that several protocols commonly regarded as “optimal” are in fact either strictly dominated by others or incomparable under rigorous information-theoretic criteria. By establishing the first adversary-aware evaluation framework for LDP protocols, this study provides both theoretical foundations and practical guidance for mechanism design and selection.

Abstract

Local Differential Privacy (LDP) has become the de facto standard for privacy-preserving data collection in large-scale systems, in particular for the purpose of estimating frequencies. However, the current research landscape lacks a systematic and principled way to compare LDP protocols. The parameter $\varepsilon$ of LDP is considered the measure of privacy, but it only bounds worst-case distinguishability. Other comparisons rely on utility-driven analyses, where mechanisms are ranked based on their ability to preserve data utility for a given privacy budget $\varepsilon$. Both such kinds of comparisons fail to account for the strength of protocols against diverse attacker models. In this paper, we propose a framework for analyzing LDP frequency estimation protocols through the lens of Quantitative Information Flow (QIF). By modeling LDP mechanisms as probabilistic channels, we leverage the concept of refinement (Blackwell ordering) to establish more principled classifications. This approach allows us to determine when one protocol is intrinsically superior to another for all possible adversaries, and to discuss the implications for utility. In particular, our analysis uncovers cases where protocols previously deemed "optimal" are, in fact, incomparable with, or strictly dominated by, other protocols. We provide a formal QIF-based treatment of seven state-of-the-art protocols, including Generalized Randomized Response (GRR), local hashing variants (BLH, OLH), unary encoding schemes (SUE, OUE), and Thresholding with Histogram Encoding (THE). This perspective bridges the gap between the LDP and formal methods communities and enables principled, adversary-aware reasoning about locally private systems.

Problem

• Local Differential Privacy
• Quantitative Information Flow
• Privacy Comparison
• Attacker Models
• Protocol Refinement

Innovation

• Quantitative Information Flow
• Local Differential Privacy
• Blackwell ordering
• Privacy Mechanism Comparison
• Adversary-aware Analysis