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SUNRISE Scientific Publication

To Pass or Not to Pass: Privacy-Preserving Physical Access Control

SUNRISE partner AIT published a paper in Elsevier Computers & Security titled, ‘To Pass or Not to Pass: Privacy-Preserving Physical Access Control,’ in October 2023.


Anonymous or attribute-based credential (ABC) systems are a versatile and important cryptographic tool to achieve strong access control guarantees while simultaneously respecting the privacy of individuals. A major problem in the practical adoption of ABCs is their transferability, i.e., such credentials can easily be duplicated, shared or lent. One way to counter this problem is to tie ABCs to biometric features of the credential holder and to require biometric verification on every use. While this is certainly not a viable solution for all ABC use-cases, there are relevant and timely use-cases, such as vaccination credentials as widely deployed during the COVID-19 pandemic. In such settings, ABCs that are tied to biometrics, which we call Biometric-Bound Attribute-Based Credentials (bb-ABC), allow to implement scalable and privacy-friendly systems to control physical access to (critical) infrastructure and facilities. While there are some previous works on bb-ABC in the literature, the state of affairs is not satisfactory. Firstly, in existing work the problem is treated in a very abstract way when it comes to the actual type of biometrics. Thus, it does not provide concrete solutions which allow for assessing their practicality when deployed in a real-world setting. Secondly, there is no formal model which rigorously captures bb-ABC systems and their security requirements, making it hard to assess their security guarantees. With this work we overcome these limitations and provide a rigorous formalization of bb-ABC systems. Moreover, we introduce two generic constructions which offer different trade-offs between efficiency and trust assumptions, and provide benchmarks from a concrete instantiation of such a system using facial biometrics. The latter represents a contact-less biometric feature that provides acceptable accuracy and seems particularly suitable to the above use-case.

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