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We consider the problem of identification and authentication based on secret key generation from some user-generated source data (e.g., a biometric source). The goal is to reliably identify users preenrolled in a database as well as authenticate them based on the estimated secret key while preserving the privacy of the enrolled data and of the generated keys. We characterize the optimal tradeoff between the identification rate, the compression rate of the users' source data, information leakage rate, and secret key rate. In particular, we provide a coding strategy based on layered random binning which is shown to be optimal. In addition, we study a related secure identification/authentication problem where an adversary tries to deceive the system using its own data. Here the optimal tradeoff between the identification rate, compression rate, leakage rate, and exponent of the maximum false acceptance probability is provided.The results reveal a close connection between the optimal secret key rate and the false acceptance exponent of the identification/authentication system.

We consider problems of authentication using secret key generation under a privacy constraint on the enrolled source data. An adversary who has access to the stored description and correlated side information tries to deceive the authentication as well as learn about the source. We characterize the optimal tradeoff between the compression rate of the stored description, the leakage rate of the source data, and the exponent of the adversary's maximum false acceptance probability. The related problem of secret key generation with a privacy constraint is also studied where the optimal tradeoff between the compression rate, leakage rate, and secret key rate is characterized. It reveals a connection between the optimal secret key rate and security of the authentication system.

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