Robust techniques for evaluating biometric cryptographic key generators
Lucas Ballard · 2008
Humans are unable to generate and remember strong secrets, and thus have difficulty managing cryptographic keys. To address this problem, numerous proposals have been suggested to enable people to reliably generate high-entropy cryptographic keys from measurements of their physiology or behavior. Typically, evaluators argue that these Biometric Cryptographic Key Generators (BKGs) achieve some notion of security, for example, that the biometric input resists forgery, or that the keys have high entropy. Unfortunately, despite these arguments, many BKGs succumb to attacks in practice. The goal of this work is to understand why typical security arguments fail to identify practical attacks. We revisit the security requirements of BKGs and show that common arguments overlook practical subtleties. We provide examples of such oversights by examining three general classes of adversaries. First, we study the impact of humans who can replicate other users ’ biometrics with high accuracy, and demonstrate why typical evaluation techniques fail to identify these forgers. Second, we explore Generative techniques that combine information about a target user with population statistics to create forgeries. We show that these forgeries can subvert BKGs with high likelihood. Third, we propose an