Quantitative information flow under generic leakage functions and adaptive adversaries

TL;DR

This paper introduces a comprehensive model for analyzing quantitative information flow with generic leakage functions and adaptive adversaries, providing theoretical insights and computational methods for optimal strategy design.

Contribution

It presents a unified model for QIF analysis, establishes conditions for adaptive strategies, compares adaptive and non-adaptive efficiencies, and formulates a Bellman equation for optimal leakage computation.

Findings

Adaptive strategies are as efficient as non-adaptive ones up to a bounded expansion.

Maximum leakage can be computed using a Bellman equation.

The model generalizes many existing QIF frameworks.

Abstract

We put forward a model of action-based randomization mechanisms to analyse quantitative information flow (QIF) under generic leakage functions, and under possibly adaptive adversaries. This model subsumes many of the QIF models proposed so far. Our main contributions include the following: (1) we identify mild general conditions on the leakage function under which it is possible to derive general and significant results on adaptive QIF; (2) we contrast the efficiency of adaptive and non-adaptive strategies, showing that the latter are as efficient as the former in terms of length up to an expansion factor bounded by the number of available actions; (3) we show that the maximum information leakage over strategies, given a finite time horizon, can be expressed in terms of a Bellman equation. This can be used to compute an optimal finite strategy recursively, by resorting to standard methods like backward induction.