A Formal Privacy Analysis of Identity Management Systems

With the growing amount of personal information exchanged over the Internet, privacy is becoming more and more a concern for users. In particular, personal information is increasingly being exchanged in Identity Management (IdM) systems to satisfy the increasing need for reliable on-line identification and authentication. One of the key principles in protecting privacy is data minimization. This principle states that only the minimum amount of information necessary to accomplish a certain goal should be collected. Several “privacy-enhancing” IdM systems have been proposed to guarantee data minimization. However, currently there is no satisfactory way to assess and compare the privacy they offer in a precise way: existing analyses are either too informal and high-level, or specific for one particular system. In this work, we propose a general formal method to analyse privacy in systems in which personal information is communicated and apply it to analyse existing IdM systems. We first elicit privacy requirements for IdM systems through a study of existing systems and taxonomies, and show how these requirements can be verified by expressing knowledge of personal information in a three-layer model. Then, we apply the formal method to study four IdM systems, representative of different research streams, analyse the results in a broad context, and suggest improvements. Finally, we discuss the completeness and (re)usability of the proposed method.