## 1 Introduction  
With the rapid development of biometrics-based recognition technology, biometric images (e.g., face, iris, fingerprint, iris, retina) can be adopted to generate a biometric key (bio-key), which is used as a user's physical identity in the fields of IoT, blockchain, and cloud computing [1, 2, 3]. In recent years, people have paid more attention to the privacy and security of biometric data. Once the bio-key generation system exposes biometric data, attackers can utilize this data to access the server for stealing the user's private information, which leads to sensitive information leakage and financial loss. In addition, biometric data is permanently associated with the user's natural identity, thus revocation of the user's biometric trait is impossible [4]. Therefore, for a secure and reliable bio-key generation approach, there are three main issues to be solved. As shown in Figure 1, these issues are as follows:  
1. Accuracy issue. Generated bio-key is affected by some variations of the biometric image such as illumination, blur, and pose. 2. Security issue. Since the stored helper data or auxiliary information has the risk of information leakage, an attacker can reconstruct biometric data from the helper data in a database. 3. Privacy issue. Once the bio-key is leaked, an attacker can use the leaked key to achieve authentication in other applications. Moreover, a new bio-key cannot be regenerated to deploy the application system. Many researchers have explored different approaches to solve these issues. The traditional bio-key generation scheme is divided into three categories [5,6]: key binding, key generation, and secure sketch and fuzzy extractor, which face the following challenges:  
1. For the key binding scheme, biometric data and cryptographic key are bound to generate the helper data for hiding the biometric information. There are two typical instances of this scheme: fuzzy commitment and fuzzy vault. On the one hand, [PERSON] et al. [7] demonstrate the fuzzy commitment approach leaks the biometric information. On the other hand, [PERSON] et al. [8] show that multiple helper data of the fuzzy vault can be filtered chaff points to retrieve bio-key via the correlation attack. Thus, they both face the information leakage challenge. 2. For the key generation scheme, biometric data is used to directly generate bio-keys without the external auxiliary information. However, the accuracy of the generated bio-key is sensitive to intra-user variations. In addition, since the input biometric data is continuous, generating a high-entropy bio-key is difficult [9]. Therefore, there is still room for improvement in accuracy and security. 3. For the secure sketch and fuzzy extractor schemes, they both use auxiliary information to restore the bio-key. Nevertheless, [PERSON] et al. [10] and [PERSON] et al. [11] demonstrate that these two schemes have information leakage risk. Furthermore, multiple uses of helper data cause privacy risk [12]. With the rapid development of deep learning in the field of biometric recognition [13,14], [PERSON] et al. [15] use a deep neural network (DNN) to learn maximum entropy binary (MEB) codes from biometric images. [PERSON] et al. [16] design a bio-key generation method based on a convolutional neural network (CNN) and a recurrent neural network (RNN). [PERSON] et al. [17] propose a DNN framework to learn robust biometric features for improving authentication accuracy. However, these methods based on the DNN or CNN scheme did not consider the mentioned challenges of security and privacy. To overcome the above challenges, we propose a secure bio-key generation method based on deep learning. The proposed approach is used to improve security and privacy  
Figure 1: Illustration of security, privacy, and accuracy issues in the biometric key generation methods. while maintaining accuracy in the biometric authentication system. Specifically, it consists of three parts: (1) a biometrics mapping network; (2) a random permutation module; and (3) a fuzzy commitment module.