Personal health records (PHRs) are private and vital assets for every patient. There have been introduced many works on various aspects of managing and organizing the PHR so far. However, there is an uncertain remaining issue for the role of PHR in emergencies. In a traditional emergency access system, the patient cannot give consent to emergency staff for accessing his/her PHR. Moreover, there is no secured record management of patient's PHR, which reveals highly confidential personal information, such as what happened, when, and who has access to such information. This paper proposes an emergency access control management system (EACMS) based on permissioned blockchain hyperledger fabric and hyperledger composer. In the proposed system, we defined some rules using the smart contracts for emergency condition and time duration for the emergency access PHR data items that patient can assign some limitations for controlling the PHR permissions. We analyzed the performance of our proposed framework by implementing it through the hyperledger composer based on the response time, privacy, security, and accessibility. The experiments confirm that our framework provides better efficiency compared with the traditional emergency access system.

Blockchain technology is the most trusted all-in-one cryptosystem that provides a framework for securing transactions over networks due to its irreversibility and immutability characteristics. Blockchain network, as a decentralized infrastructure, has drawn the attention of various startups, administrators, and developers. This system preserves transactions from tampering and provides a tracking tool for tracing past network operations. A personal health record (PHR) system permits patients to control and share data concerning their health conditions by particular peoples. In the case of an emergency, the patient is unable to approve the emergency staff access to the PHR. Furthermore, a history record management system of the patient’s PHR is required, which exhibits hugely private personal data (e.g., modification date, name of user, last health condition, etc.). In this paper, we suggest a healthcare management framework that employs blockchain technology to provide a tamper protection application by considering safe policies. These policies involve identifying extensible access control, auditing, and tamper resistance in an emergency scenario. Our experiments demonstrated that the proposed framework affords superior performance compared to the state-of-the-art healthcare systems concerning accessibility, privacy, emergency access control, and data auditing.

With the popularity of smartphones and widespread use of high-speed Internet, social media has become a vital part of people’s daily life. Currently, text messages are used in many applications, such as mobile chatting, mobile banking, and mobile commerce. However, when we send a text message via short message service (SMS) or social media, the information contained in the text message transmits as a plain text, which exposes it to attacks. In some cases, this information may be confidential, such as banking credentials, secret missions, and confidential appointments; moreover, it is a major drawback to send such information via SMS or social media, as neither provides security before transmission. In this study, we propose a novel text steganography technique called AITSteg, which provides end-to-end security during the transmission of text messages via SMS or social media between end users. The AITSteg technique is evaluated by considering a trusted scenario. We then evaluate the efficiency of the proposed technique in terms of embedding capacity, invisibility, robustness, and security. The experiments confirm that the AITSteg is able to prevent various attacks, including man-in-the-middle attacks, message disclosure, and manipulation by readers. Moreover, we compare the experimental results with the existing techniques to show the superiority of the proposed technique. To the best of our knowledge, this is the first text steganography technique that provides end-to-end secure transmission of the text message using symmetric keys via social media.

Modern text hiding is an intelligent programming technique which embeds a secret message/watermark into a cover text message/file in a hidden way to protect confidential information. Recently, text hiding in the form of watermarking and steganography has found broad applications in, for instance, covert communication, copyright protection, content authentication, etc. In contrast to text hiding, text steganalysis is the process and science of identifying whether a given carrier text file/message has hidden information in it, and, if possible, extracting/detecting the embedded hidden information. This paper presents an overview of state of the art of the text hiding area, and provides a comparative analysis of recent techniques, especially those focused on marking structural characteristics of digital text message/file to hide secret bits. Also, we discuss different types of attacks and their effects to highlight the pros and cons of the recently introduced approaches. Finally, we recommend some directions and guidelines for future works.

The traditional security risk monitoring technology cannot adapt to cyber-physical power systems (CPPS) concerning evaluation criteria, real-time monitoring, and technical reliability. The aim of this paper is to propose and implement a log analysis architecture for CPPS to detect the log anomalies, which introduces the distributed streaming processing mechanism. The processing mechanism can train the network protocol feature database precisely over the big data platform, which improves the efficiency of the network in terms of log anomaly detection. Moreover, we propose an ensemble prediction algorithm based on time series (EPABT) considering the characteristics of the statistical log analysis to predict abnormal features during the network traffic analysis. We then present a new asymmetric error cost (AEC) evaluation criterion to meet the characteristics of CPPS. The experimental results demonstrate that the EPABT provides an efficient tool for detecting the accuracy and reliability of abnormal situation prediction as compared with the several state-of-the-art algorithms. Meanwhile, the AEC can effectively evaluate the differences in the cost between the high and low prediction results. To the best of our knowledge, these two algorithms provide strong support for the practical application of power industrial network security risk monitoring.