A Real-Time Biometric Encryption Scheme Based on Fuzzy Logic for IoT

Abstract

When milliards of smart devices are connected to the Internet using the Internet of Things (IoT), robust security methods are required to deliver current information to the objects. Using IoT, the user can be accessed via smart device applications at any time and any place, which challenges IoT security and privacy. From security point of view, users and smart devices should have secure communication channel and digital ID. Authentication is the first step towards any security action. Biometric-based authentication can ensure higher security for developing secure access. In this paper, fingerprint is used as the biometric factor. After scanning the fingerprint using the cellphone’s camera, the image is transmitted to the authentication system. Since the comparison time increases after increasing the database volume, instead of storing the scanned fingerprint image, some key features of the scanned fingerprint are extracted and transmitted to the learning system of the convolutional neural network for detection and authentication and stored in the database. In the user authentication phase, the authentication keys are identified and the passcodes for the user of interest are extracted, and zero code is sent to the forged people; finally, the passcode is examined to check if the user is legal or illegal. In this step, the legal codes for fuzzy encoding of the image and text information are activated, and encryption is carried out in multiple steps depending on the number of members. The final code is compressed using Huffman coding and used for transmission to the network or storage. The proposed method is tested in MATLAB, and the results show that an excellent security is achieved using this cascade encryption method. Conclusively, the proposed hybrid coding technique reduces the information volume by 15.9%.