Abstract
Privacy plays an important role in biometric authentication systems. Touch authentication systems have been widely used since touch devices reached their current level of development. In this work, a fuzzy commitment scheme (FCS) is proposed based on deep learning (DL) to protect the touch-gesture template in a touch authentication system. The binary Bose–Ray-Chaudhuri code (BCH) is used with FCS to deal with touch variations. The BCH code is described by the triplet (n, k, t) where n denotes the code word’s length, k denotes the length of the key and t denotes error-correction capability. In our proposed system, the system performance is investigated using different lengths k. The learning-based approach is applied to extract touch features from raw touch data, as the recurrent neural network (RNN) is used based on a convolutional neural network (CNN). The proposed system has been evaluated on two different touch datasets: the Touchalytics dataset and BioIdent dataset. The best results obtained were with a key length k = 99 and n = 255; the false accept rate (FAR) was 0.00 and false reject rate (FRR) was 0.5854 for the Touchalytics dataset, while the FAR was 0.00 and FRR was 0.5399 with the BioIdent dataset. The FCS shows its effectiveness in dynamic authentication systems, as good results are obtained and compared with other works.
Highlights
A biometric authentication system is a system that uses a user’s biometric data to identify them
We used the hashlib library for biometric template security
We propose an fuzzy commitment scheme (FCS) to secure a template of touch gestures in a touch authentication system
Summary
A biometric authentication system is a system that uses a user’s biometric data to identify them. It is difficult to exchange a user’s biometric data that has been stolen [1,2] For these reasons, protecting the biometric template is important in order to increase the security level of biometric authentication systems [3,4]. A biometric cryptosystem is a system that creates biometric cryptographic keys (BCKs) and combines these keys with user’s biometrics in order to increase the security level and keep the user’s information safe from identity theft [7,8]. A biometric cryptosystem is a system that is used to protect the biometric template by combining a cryptography key with the biometrics template [20,21] In this system, neither the biometric data nor the key is stored in the database. The helper data is used to retrieve the key when the genuine biometric data is given [22]
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