A Metric for Anonymity based on Subjective Logic

Anonymity metrics have been proposed to evaluate anonymity preserving systems by estimating the amount of information displayed by these systems due to vulnerabilities. A general metric for anonymity that assess the latter systems according to the mass and quality of information learned by an attacker or a collaboration of attackers is proposed here. The proposed metric is based on subjective logic, a generalization of evidence and probability theory. As a consequence, we proved based on defined scenarios that our metric provide a better interpretation of uncertainty in the measure and it is extended to combine various sources of information using subjective logic operators. Also, we demonstrate that two factors: trust between collaborating attackers and time can influence significantly the metric result when taking them into consideration