Axioms for Information Leakage

Abstract

Quantitative information flow aims to assess and control the leakage of sensitive information by computer systems.A key insight in this area is that no single leakage measure is appropriate in all operational scenarios; as a result, many leakage measures have been proposed, with many different properties. To clarify this complex situation, this paper studies information leakage axiomatically, showing important dependencies among different axioms. It also establishes a completeness result about the -leakage family, showing that any leakage measure satisfying certain intuitively-reasonable properties can be expressed as aleakage.Index Terms-information flow, -vulnerability, information theory, confidentiality.

Leakage and Protocol Composition in a Game-Theoretic Perspective

“…It is known from the literature [5] that the posterior vulnerability is a convex function of π. Namely, for any channel C, any family of distributions {π i }, and any set of convex coefficients {c i }, we have:…”

“…In this paper we consider a very general notion of vulnerability, following [5], and we define a vulnerability V to be any continuous and convex function of type DX → R. It has been shown in [5] that these functions coincide with the set of g-vulnerabilities, and are, in a precise sense, the most general information measures w.r.t. a set of basic axioms.…”

“…A central notion in this model is that of entropy, but here we use its converse, vulnerability, which represents the magnitude of the threat. In order to derive results as general as possible, we adopt the very comprehensive notion of vulnerability as any convex and continuous function, as used in [5,8]. This notion has been shown [5] to subsume most information measures, including Bayes vulnerability (aka minvulnerability, aka (the converse of) Bayes risk) [10,27], Shannon entropy [26], guessing entropy [22], and g-vulnerability [6].…”

“…Indeed, nowadays there is a tendency to use abstract channels [5,23], which capture exactly the important behavior with respect to any form of leakage. In this paper, however, we cannot use abstract channels because the hidden choice operator needs a concrete representation in order to be defined unambiguously.…”

Universal Optimality of Apollonian Cell Encoders

“…Entropy can conveniently represent the information that the adversary has about the secret message before and after an attack. Consequently entropy can be used to quantify the information gained by the adversary due to the attack [19], [20], [21], [5], [22], [23]. The entropy of a probability distribution ρ over a support set X = {x 1 , .…”