This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.

Electronic health records (EHRs) replaced the old paper-based systems to make patient data more accurate, reliable and more accessible. Yet, the EHRs system requires high transmission cost, energy, and waste of time for both doctors and patients. Furthermore, EHRs security presents a serious issue threatening the patient’s privacy. Most of the third-party hosting systems have some issues related to the users’ privacy and data security. Hence, it is necessary to restrict the access control policies and develop efficient mechanisms for cloud-based EHRs data. In this paper, a Sensitive and Energetic Access Control (SE-AC) mechanism is proposed for managing the cloud-hosted EHRs and providing a fine-grained access control even in critical situations.. The proposed mechanism ensures the confidentiality of the patient’s data, where only authorized individuals to have permission to be able to edit or review certain of the patient’s data. Each EHR data is encrypted by the managing authority before submitting to the cloud storage. The requesting user can get dynamically changing permissions based on authentication and context attributes. Additionally, seven major aspects have been quantified to assess the operation of any access control that could be deployed in IoT. The security analysis indicates that the SE-AC mechanism is secure and will prevent any unauthorized access. The results show exceptional compatibility and performance with different setups and configuration.

In this article, we address the problem of confidentiality of keyframes, which are extracted from diagnostic hysteroscopy data using video summarization. We propose an image color coding method aimed at increasing the security of keyframes extracted from diagnostic hysteroscopy videos. In this regard, we use a 2D logistic map to generate the cryptographic keys sequences, which relies on mixing and cascading the orbits of the chaotic map in order to generate the stream keys for the encryption algorithm. The encrypted images produced by our proposed algorithm exhibit randomness behavior, providing a high-level of security for the keyframes against various attacks. The experimental results and security analysis from different perspectives verify the superior security and high efficiency of our proposed encryption scheme compared to other state-of-the-art image encryption algorithms. Furthermore, the proposed method can be combined with mobile-cloud environments and can be generalized to ensure the security of cloud contents as well as important data during transmission.

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.