Privacy-preserving Hybrid Cryptosystem Based on Chaos Theory and ECC Algorithm

Data encryption is a practical approach to protect biomedical image information security. Towards the massive amount of data generated in the medical field, chaotic model cryptosystems, with high sensitivity to initial conditions and overall stability and randomness of the system, are inevitably becoming an appropriate platform for communication protection. This paper proposes a privacy-preserving Hybrid encryption scheme based on Chaos theory and ECC Algorithm (HCEA) for the secure delivery of medical data. Specifically, we employ the logistic map with randomness characteristics to protect published data privacy against the complex network environment and other non-subscribers. The proposed cryptosystem can essentially achieve the secrecy effect of a “one-time pad.” To achieve double encryption of textual information and keys, we propose an improved image steganography technique with secondary scrambling encryption of carrier images by the MLNCML system, enhancing encryption efficiency and security. Different from existing standard LSB image encryption methods, the HCEA can encrypt initial parameters of logistic and MLNCML with the asymmetric encryption ECC algorithm while guaranteeing the security of key distribution in complex network environments. The security proof and performance evaluation show that the proposed HCEA scheme is secure in medical data transmission and effective in practice.