A data security scheme based on EEG characteristics for body area networks

Abstract

Body area network (BAN) is a body-centered network of wireless wearable devices. As the basic technology of telemedicine service, BAN has aroused an immense interest in academia and the industry and provides a new technical method to solve the problems that exist in the field of medicine. However, guaranteeing full proof security of BAN during practical applications has become a technical issue that hinders the further development of BAN technology. In this article, we propose a data encryption method based on electroencephalogram (EEG) characteristic values and linear feedback shift register (LFSR) to solve the problem of data security in BAN. First, the characteristics of human EEG signals were extracted based on the wavelet packet transform method and as the MD5 input data to ensure its randomness. Then, an LFSR stream key generator was adopted. The 128-bit initial key obtained through the message-digest algorithm 5 (MD5) was used to generate the stream key for BAN data encryption. Finally, the effectiveness of the proposed security scheme was verified by various experimental evaluations. The experimental results showed that the correlation coefficient of data before and after encryption was very low, and it was difficult for the attacker to obtain the statistical features of the plaintext. Therefore, the EEG-based security scheme proposed in this article presents the advantages of high randomness and low computational complexity for BAN systems.