Biometric authentication offers a secure and convenient way to verify user identity, but traditional systems often require storing sensitive biometric templates, posing significant privacy risks. This paper explores the use of Locality-Sensitive Hashing (LSH) combined with zk-protocols to enable privacy-preserving fingerprint authentication without storing or exposing raw biometric data.
The article presents an advanced method of fingerprint identification based on convolutional neural network (CNN) technology. This work elaborately describes the development and implementation process of a specialized CNN architecture for detecting and verifying the authenticity of fingerprints. Utilizing the comprehensive Socofing dataset allowed for an in -depth analysis of the model’s ability to distinguish between genuine and fabricated fingerprints, where the model demonstrated impressive accuracy – up to 98.964%.
The article analyzes the models, methods and means of authentication of computer users by their handwriting keyboard. Identified deficiencies of existing mathematical models and methods of data processing keyboard writing. Formulated topical problems of further research in the field of information security.