Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

The authentication of Internet of Things (IoT) devices based on the Physical Unclonable Function (PUF) is widely adopted in the information security domain. However, the leakage of PUF responses in an authentication system reduces its privacy and security. To improve its security, we can utilize the Elliptic Curve Cryptography (ECC) algorithm with different key lengths to encrypt the PUF response arbitrarily. Point multiplication is the most time-consuming operation in ECC because of its complex calculation process, which seriously affects the efficiency of the PUF response encryption. In order to solve this problem, a point multiplier based on binary field with reconfigurable key lengths of 233, 283, 409 and 571 is designed in this paper. In our method, by reusing the underlying computing units, the resources needed for point multiplication are effectively reduced. What it is more innovative is that double point multiplication operations with a key length of less than 283 bits can be performed simultaneously in the elaborate designed point multiplication circuit, which can effectively speed up the encryption process of ECC. The circuit is implemented on Xilinx Virtex-6 FPGA. The experiment results show the single point multiplication times of 233, 283, 409 and 571 key lengths are 19.33, 22.36, 41.36 and 56.5 μs, respectively, under the clock frequency of 135 MHz. In addition, it only needs 19.33 μs to perform two-point multiplication operations when the key length is 233 bits at the same time. When the key length is 283 bits, the point multiplication operation can be performed twice in 22.36 μs.