Application of Canvas Fingerprint-Based Identity Authentication Technology in Smart Grid Data Security

Smart grids are the core infrastructure of power systems, and the security of their data is of great significance to the normal operation of power systems and the privacy of users. This paper designs a complete Canvas fingerprint-based identity authentication process, including the initialization of Canvas elements, the generation of drawing commands, the extraction and analysis of image data, the calculation of feature points, and the final generation of Canvas fingerprints. By simulating attack scenarios, this paper conducts a security assessment, privacy protection capability test, and performance efficiency analysis of the Canvas fingerprint method and three other traditional methods (data aggregation technology, group signature technology, and homomorphic encryption technology). The results show that the Canvas fingerprint technology demonstrates significant advantages in user identity protection, data anonymization, encryption strength, and user behavior protection. Particularly in terms of system stability and attack resistance, the Canvas fingerprint method's comprehensive security score of 87.648 is significantly higher than that of other methods. In addition, the Canvas fingerprint technology also performs outstandingly in terms of performance efficiency.