Chaos-Based Index-of-Min Hashing Scheme for Cancellable Biometrics Security

Abstract

Cancellable biometrics is essential for preserving sensitive biometric information from potential exposure. Existing studies usually convert real-valued biometric vectors into protected templates by randomly generated transformation keys. However, this way is realized by the built-in functions of the cancellable biometric system, which creates vulnerabilities for cancellable biometric schemes. In this paper, we propose a novel chaos-based Index-of-Min cancellable biometric scheme, named C-IoM, for privacy-preserving template updates in biometric technique. Specifically, we first design a chaos-based cancellable biometric framework to ensure the security and privacy of the biometric template. Second, we develop a secure random chaos seed generation algorithm, which non-linearly converts the biometric vectors into protected templates and conceals biometric dimensional information. Further, we craft a sliding window selection mechanism to choose the input biometric features, allowing each feature data to fully participate in the generation of protected templates through sliding intervals. Theoretical analysis confirms that the C-IoM satisfies the criteria of irreversibility, revocability, unlinkability, and performance preservation in cancellable biometrics. Extensive experiments on LFW, CFPW, and CASIA-V5 datasets demonstrate the security of the proposed framework in protecting biometric data as well as the superiorities over state-of-the-art schemes.