A Survey on Homomorphic Encryption for Biometrics Template Security Based on Machine Learning Models

Recent years have seen increased interest in research on biometric template protection due to the widespread use of biometric authentication systems. For providing security to biometric templates, there is a procedure known as Fully Homomorphic Encryption that protects biometric templates from the malicious server environment. Users have more customization options with a biometric authentication system than a password or token. The most popular biometric modalities include fingerprints, iris scans, facial images, etc. Biometric modalities offer security on IoMT -based systems. Face recognition is one of the most often utilized biometric authentication methods in societal structure. Face recognition technology has made enormous strides in recent years. Here, we examine the viability of securing a database of iris templates using a methodology based on fully homomorphic Encryption. By directly matching templates in the encrypted domain, this framework is designed to protect confidentiality and restrict information from leaking from the templates while preserving their utility. We also investigate various classification techniques on machine learning models to achieve improved accuracy with shorter execution times. The aggregate verification vector assists in confirming the accuracy of the computed classification result, and the CKKS technique ensures confidentiality for the biometric templates. This study provides a plethora of information on fully homomorphic biometric authentication, containing a wide assortment of algorithms that satisfy homomorphic Encryption and various methods for extracting the biometric-related template.