Enhancing Privacy Through DMMA: Decision-Making Method for Authentication
Maksym Slavnenko, Yevhen Zolotavkin, Jongkil Jay Jeong, Veronika Kuchta, Robin Doss · 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · 2021
Attribute-Based Authentication (ABA) is becoming more prevalent in everyday interactions. In this paper, we propose the Decision-Making Method for Authentication (DMMA) to address the privacy concerns in ABA. The need for DMMA is supported through multiple observations. First, in practice, the indistinguishability of crypto-proof-based assertions (that are posessed by different users) fails with non-zero probability. This explains why cryptographic means alone are insufficient to provide a substantial level of unlinkability in ABA systems with n users. Second, each user in ABA possesses multiple credentials: they can be used interchangeably to get access to the service(s) which is provided by a relying party (RP). DMMA addresses the challenge of interchangeable usage. As an initial step, we synthesized the criterion of unlinkability: it is based on the definitions of international standard ISO 27551 as well as the information theoretic measure of conditional entropy. We then use that criterion to formalize the task of authentication as a non-cooperative coordination game. In this game, players (targets of the attack) maximize their utilities by using their assertions interchangeably. The experiment demonstrates that a number of equilibria with substantially higher unlinkability can be achieved. Unlinkability vary depending on: i) the information (and its trustworthiness) about the moves of the other players in the game; ii) the statistical distribution of user attributes. DMMA demonstrates how users may be provided recommendations over the optimal selection of assertions for ABA. These recommendations can have a practical impact if DMMA is implemented as a feature within Digital Credential Wallets (DCWs).