Abstract:
Electronic health (e-health) networks enable users to enjoy convenient, flexible, and low-cost medical services at home, so they attract great attention and spread into t...Show MoreMetadata
Abstract:
Electronic health (e-health) networks enable users to enjoy convenient, flexible, and low-cost medical services at home, so they attract great attention and spread into the market quickly. In e-health networks, large amounts of various health data including personal privacy information and physiological signals are transmitted, which raises security risks. To protect the health data transmitted in e-health networks, steganography-based solutions have been widely researched. Although existing steganography-based solutions successfully hide health data in physiological signals such as electrocardiograms (ECG), forward secrecy is not fully considered. This means that adversaries are able to extract users’ health data hidden in previous stego signals by using compromised long-term secrets. Moreover, to reduce communication overhead, compression techniques are introduced in some steganography-based methods. However, the imperceptibility and embedding capacity of these solutions are sacrificed. To solve the above issues, in this study, we adopt Singular Value Decomposition (SVD) and the Bose-Chaudhuri-Hocquenghem (BCH) codes to design an efficient and secure health data propagation scheme based on steganography and compression. In our design, the BCH codes are used to update the encryption key and change the embedding locations in each steganography process, thus achieving forward secrecy and further enhancing the security of steganography. Moreover, a two-stage compression method is proposed in our scheme to compress the signals during signal processing and compression phases, which effectively reduces the communication overhead. Security analysis and the experimental results show that our proposed scheme enhances security while achieving an elaborate balance between imperceptibility, embedding capacity, and compression.
Published in: IEEE/ACM Transactions on Networking ( Volume: 32, Issue: 2, April 2024)