A secure and private ensemble matcher using multi-vault obfuscated templates

• URL: http://arxiv.org/abs/2404.05205v2
• Date: Mon, 12 Aug 2024 14:42:48 GMT
• Title: A secure and private ensemble matcher using multi-vault obfuscated templates
• Authors: Babak Poorebrahim Gilkalaye, Shubhabrata Mukherjee, Reza Derakhshani,
• Abstract summary: Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation. Biometric template security and secure matching are among the most sought-after features of modern biometric systems. This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security.
• Score: 1.3518297878940662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation. This technology holds immense potential for biometrics, including for securing sensitive and personally identifiable information. Given the irrevocability of biometric samples and mounting privacy concerns, biometric template security and secure matching are among the most sought-after features of modern biometric systems. This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security. Our approach utilizes synthetic facial images generated by a Generative Adversarial Network (GAN) as "random chaff points" within a secure vault system. Our method creates n sub-templates from the original template, each obfuscated with m GAN chaff points. During verification, s closest vectors to the biometric query are retrieved from each vault and combined to generate hash values, which are then compared with the stored hash value. Thus, our method safeguards user identities during the training and deployment phases by employing the GAN-generated synthetic images. Our protocol was tested using the AT&T, GT, and LFW face datasets, achieving ROC areas under the curve of 0.99, 0.99, and 0.90, respectively. Our results demonstrate that the proposed method can maintain high accuracy and reasonable computational complexity comparable to those unprotected template methods while significantly enhancing security and privacy, underscoring the potential of Generative AI in developing proactive defensive strategies for biometric systems.

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