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%.

Secure personal authentication in fog devices via multimodal rank‐level fusion

“…The task allocation model improves the adaptation and efficiency of mobile crowdsourcing systems. The location privacy-preserving scheme CKD via differential privacy-preserving and k-anonymity was proposed by Chi et al 18 While the trade-off between service quality and privacy security is successfully resolved, this approach needs to address the social relationship between workers in terms of privacy.…”

“…The task allocation model improves the adaptation and efficiency of mobile crowdsourcing systems. The location privacy‐preserving scheme CKD via differential privacy‐preserving and k‐anonymity was proposed by Chi et al 18 While the trade‐off between service quality and privacy security is successfully resolved, this approach needs to address the social relationship between workers in terms of privacy. A multi‐dimensional quality‐driven service recommendation with a privacy‐preservation model was proposed by Zhong et al 19 The experimental result demonstrated better feasibility in terms of multiple quality of service dimensions with privacy‐preserving edge service recommendations, but it was unsuitable for real‐world experiments.…”

HGSSA-bi LSTM: A Secure Multimodal Biometric Sensing Using Optimized Bi-Directional Long Short-Term Memory with Self-Attention

“…2 Designing security system has given increasing consideration to person verification based on biometric characteristics. 3 The performance requirements in realworld systems cannot be satisfied by single biometric characteristic. The majority of biometric systems have a high false rejection rate (FFR) and are neither user-friendly nor confident with users.…”