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One of the main challenges faced by Biometric-based authentication systems isthe need to offer secure authentication while maintaining the privacy of thebiometric data. Previous solutions, such as Secure Sketch and Fuzzy Extractors,rely on assumptions that cannot be guaranteed in practice, and often affect theauthentication accuracy.  In this paper, we introduce HoneyFaces: the concept of adding a large set ofsynthetic faces (indistinguishable from real) into the biometric "passwordfile". This password inflation protects the privacy of users and increases thesecurity of the system without affecting the accuracy of the authentication. Inparticular, privacy for the real users is provided by "hiding" them among alarge number of fake users (as the distributions of synthetic and real facesare equal). In addition to maintaining the authentication accuracy, and thusnot affecting the security of the authentication process, HoneyFaces offerseveral security improvements: increased exfiltration hardness, improvedleakage detection, and the ability to use a Two-server setting like inHoneyWords. Finally, HoneyFaces can be combined with other security and privacymechanisms for biometric data.  We implemented the HoneyFaces system and tested it with a password filecomposed of 270 real users. The "password file" was then inflated toaccommodate up to $2^{36.5}$ users (resulting in a 56.6 TB "password file"). Atthe same time, the inclusion of additional faces does not affect the trueacceptance rate or false acceptance rate which were 93.33\% and 0.01\%,respectively.

HoneyFaces: Increasing the Security and Privacy of Authentication Using Synthetic Facial Images