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

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The problem of merging multiple-source uncertain information is a crucial issue in many applications. This paper proposes an analysis of possibilistic merging operators where uncertain information is encoded by means of product-based (or quantitative) possibilistic networks. We first show that the product-based merging of possibilistic networks having the same DAG structures can be easily achieved in a polynomial time. We then propose solutions to merge possibilistic networks having different structures and where the union of their graphs is free of cycles. Then we show how to deal with merged networks having cycles. Lastly, we handle the sub-normalization problem which reflects the presence of conflicts between different sources.