Security from uncertainty: Designing privacy-preserving verification methods using Noise

Abstract

Biometric authentication plays an increasingly prominent role in today’s products and services for verifying an individual’s identity. It is not only efficient but also practical, as it establishes a unique link to an individual through their physical and behavioral characteristics [43]. Unlike conventional authentication mechanisms like passwords or documents, biometric traits are inherent to each individual, eliminating the need to memorize additional information [64]. However, the security and privacy of biometric templates used in authentication remain primary concerns, as biometric data is strongly and irrevocably tied to an individual, as emphasized in the article [42]. In the context of remote authentication, Secure Multiparty Computation (SMC) offers a powerful solution. SMC enables two parties to interactively compute a function using their private inputs without disclosing any information except for the output itself [19]. This approach ensures that biometric template comparison is carried out in a privacy-preserving manner, enhancing both security and privacy in authentication services. In this thesis, we introduce a unique approach to iris, fingerprint, and face verification by incorporating ”noise” into the authentication process. In our work,“noise” refers to signals obtained from non-discriminatory or unreliable regions of biometric characteristics. Our extensive empirical evaluation reveals a correlation among noise features, and we leverage this correlation in a novel Secure Two-Party Computation (STPC) design. This STPC design operates on quantified uncertainty between noise features, providing informationtheoretic security. Our approach has low accuracy degradations, practical computational complexity, wide applicability making it suitable for practical real-time applications.