A privacy-preserving cryptosystem for IoT E-healthcare

Privacy preservation has become a prerequisite for modern applications in the cloud, social media, Internet of things (IoT), and E- healthcare systems. In general, health and medical data contain images and medical information about the patients and such personal data should be kept confidential in order to maintain the patients' privacy. Due to limitations in digital data properties, traditional encryption schemes over textual and structural one-dimension data cannot be applied directly to e-health data. In addition, when personal data are sent over the open channels, patients may lose privacy of data contents. Hence, a secure lightweight keyframe extraction method is highly required to ensure timely, correct, and privacy-preserving e-health services. Besides this, it is inherently difficult to achieve a satisfied level of security in a cost-effective way while considering the constraints of real-time e-health applications. In this paper, we propose a privacy preserving chaos-based encryption cryptosystem for patients' privacy protection. The proposed cryptosystem can protect patient's images from a compromised broker. In particular, we propose a fast probabilistic cryptosystem to secure medical keyframes that are extracted from wireless capsule endoscopy procedure using a prioritization method. The encrypted images produced by our cryptosystem exhibits randomness behavior, which guarantee computational efficiency as well as a highest level of security for the keyframes against various attacks. Furthermore, it processes the medical data without leaking any information, thus preserving patient's privacy by allowing only authorized users for decryption. The experimental results and security analysis from different perspectives verify the excellent performance of our encryption cryptosystem compared to other recent encryption schemes. (C) 2019 Elsevier Inc. All rights reserved.