A Privacy-Preserving Brainprint Recognition System Based on Feature Homomorphic Encryption

Yiming Zhang, Hangjie Yi, Wanzeng Kong

Abstract: In recent years, the extensive use of electroencephalogram (EEG) biometric recognition technology has raised significant privacy concerns, particularly when the biometric computation process is carried out on an untrusted server. This paper discusses the practical scenario of performing EEG biometric recognition computation on an untrusted server within EEG biometric recognition systems. To address the privacy issues in previous systems, we propose, for the first time, a privacy-preserving EEG biometric recognition systems using homomorphic encryption. This approach enables the transmission and computation of EEG data without exposing the EEG information. Additionally, due to the large volume of raw EEG data, applying homomorphic encryption to the raw EEG data would significantly increase the time and space overhead of the homomorphic computation. This paper further proposes the application of homomorphic encryption to brainprint feature, as the data volume of brainprint feature is small, resulting in a significant reduction in computational time and space overhead. We conducted a quantitative evaluation of the system’s classification accuracy, time overhead, and space overhead, and compared it with the unencrypted system. Experimental results demonstrate that our approach maintains classification accuracy, meets the real-time requirements of brainprint recognition systems in terms of time overhead, and keeps space overhead within an acceptable range, while preserving the privacy and security of user information.