Define privacy-preserving setbase drawer size standard: A ∊-closeness perspective

Shamir proposed the setbase approach as a means of improving security and privacy of the traditional biometric system. As a result of the limitation of the current setbase filling procedure, we demonstrate that there are potential privacy weaknesses due to non-default distributions on attributes inside the identity database. We introduce in this paper, the concept of ϵ-closeness as a general framework to describe quantitatively the distribution anomaly. As a consequence, we are able to formulate a privacy-preserving drawer size standard for the setbase that includes the non-default distribution cases.