Quantifying leakage in the presence of unreliable sources of information

Abstract

Belief and min-entropy leakage are two well-known approaches to quantify information flow in security systems. Both concepts stand as alternatives to the traditional approaches founded on Shannon entropy and mutual information, which were shown to provide inadequate security guarantees. In this paper we unify the two concepts in one model so as to cope with the frequent (potentially inaccurate, misleading or outdated) attackers' side information about individuals on social networks, online forums, blogs and other forms of online communication and information sharing. To this end we propose a new metric based on min-entropy that takes into account the adversary's beliefs.

Variations and Extensions of Information Leakage Metrics with Applications to Privacy Problems with Imperfect Statistical Information

“…Concisely, mutual information measures the correlation between two variables. Authors in [40] discussed how an attacker's belief change by observing the execution of a program whereas Hamadou et al [41] unified the notion of belief and leakage for an adversary. In [40], the authors introduced a metric where an adversary observes the execution of a program and consequently updates their initial belief about the private variable.…”

“…The authors did not consider the case where an adversary approximates the privacy mechanism. Authors in [41] nevertheless introduced the metrics to represent the belief of the attacker when they had different (and potentially wrong) initial beliefs regarding the distribution of the secret and consequently presented several properties to measure the accuracy and belief of the adversary. In the current paper, we assumed the approximation of the privacy mechanism.…”

“…Moreover, we have also formulated an optimization problem that results in a privacy mechanism that minimizes the worst-case leakage. Such an optimization problem was not formulated in both [40] and [41]. Another prominent measure of information leakage is min-entropy leakage [8]- [10].…”

Quantitative Analysis of Opacity in Cloud Computing Systems

“…[22], [23] proposed model checking techniques to compute the Shannon entropy leakage and the min-entropy leakage in probabilistic transition systems. [24], [25] studied the problem of information hiding in systems characterized by the presence of randomization and concurrency. [26], [27], [28], [29] used diagnosis to detect whether or not the given sequence of observed labels indicates that some unobservable fault has occurred.…”