Interoperability in Electronic Health Records (EHR) is significant for the seamless sharing of information amongst different healthcare stakeholders. Interoperability in EHR aims to devise agreements in its interpretation, access, and storage with security, privacy, and trust. A study and survey of the state-of-the-art literature, prototypes, and projects in standardization of the EHR structure, privacy-preservation, and EHR sharing are very essential. The presented work conducts a systematic literature review to address four research questions. 1) What are the different standards for common interpretation, representation, and modeling of EHR to achieve semantic interoperability? 2) What are the different privacy-preservation techniques and security standards for EHR data storage? 3) How mature is blockchain technology for building interoperable, privacy-preserving solutions for EHR storage and sharing? 4) What is the state-of-the-art for cross-chain interoperability for EHR sharing? An exhaustive study of these questions establishes the potential of a blockchain-based EHR management framework in privacy preservation, access control and efficient storage. The study also unveils challenges in the adoption of blockchain in EHR management with the state-of-the-art maturity of cross-chain interoperable solutions for sharing EHR amongst stakeholders on different blockchain platforms. The research gaps culminate in proposing a blockchain-based EHR framework called as MyBlockEHR with privacy preservation and access control design. The proposed framework employs partitioning of EHR to on-chain and off-chain storages for performance guarantees with the retrieval of valid off-chain data. The framework is deployed on the Ethereum test network with Solidity smart contracts. It is observed that different test cases on the partitioning of the EHR data, yielded better read-write throughput and effective gas price than fully on-chain storage.

Electronic Health Records (EHR) serve as a solid documentation of health transactions and as a vital resource of information for healthcare stakeholders. EHR integrity and security issues, however, continue to be intractable. Blockchain-based EHR architectures, however, address the issues of integrity very effectively. In this work, we suggest a decentralized patient-centered healthcare data management (PCHDM) with a blockchain-based EHR framework to address issues of confidentiality, access control, and privacy of record. This patient-centric architecture keeps the patient at the center of control for secured storage of EHR data. It is effective in the storage environment with the interplanetary file system (IPFS) and blockchain technology. In order to control unauthorized users, the proposed secure password authentication-based key exchange (SPAKE) implements smart contract-based access control to EHR transactions and access policies. The experimental setup comprises four hyperledger fabric nodes with level DB database and IPFS off-chain storage. The framework was evaluated using the public hepatitis dataset, with parameters such as block creation time, transactional computational overhead with encryption key size, and uploading/downloading time with EHR size. The framework enables patient-centric access control of the EHR with the SPAKE encryption algorithm.

Social media platforms are popular for expressing personal views, emotions and beliefs. Social media platforms are influential for propagating extremist ideologies for group-building, fund-raising, and recruitment. To monitor and control the outreach of extremists on social media, detection of extremism in social media is necessary. The existing extremism detection literature on social media is limited by specific ideology, subjective validation methods, and binary or tertiary classification. A comprehensive and comparative survey of datasets, classification techniques, validation methods with online extremism detection tool is essential. The systematic literature review methodology (PRISMA) was used. Sixty-four studies on extremism research were collected, including 31 from SCOPUS, Web of Science (WoS), ACM, IEEE, and 33 thesis, technical and analytical reports using Snowballing technique. The survey highlights the role of social media in propagating online radicalization and the need for extremism detection on social media platforms. The review concludes lack of publicly available, class-balanced, and unbiased datasets for better detection and classification of social-media extremism. Lack of validation techniques to evaluate correctness and quality of custom data sets without human interventions, was found. The information retrieval unveiled that contemporary research work is prejudiced towards ISIS ideology. We investigated that deep learning based automated extremism detection techniques outperform other techniques. The review opens the research opportunities for developing an online, publicly available automated tool for extremism data collection and detection. The survey results in conceptualization of architecture for construction of multi-ideology extremism text dataset with robust data validation techniques for multiclass classification of extremism text.

Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

The advancement and comprehensive adoption of blockchain technology has led to momentous progression in data-critical areas such as DeFi, health sector, defense, supply chain, and beyond. Nevertheless, beside this growth, there has been a distinguished rise in both the prevalence and diversification of security vulnerabilities. Recent attacks have exploited these vulnerabilities in targeting different layers of the blockchain architecture. This paper centralizes on the security vulnerabilities, attacks, and challenges faced by blockchain systems, presenting an exhaustive taxonomical classification of blockchain layered security. It classifies diverse security issues across the different layers of blockchain architecture. With the widespread adoption of blockchain in enterprise applications, smart contracts have become a vital target for attackers, leading to significant financial and data losses. Additionally, as divergent applications are deployed across multiple blockchains, the demand for cross-chain contracts is getting progressively imperative. Investigating the security of smart contracts and cross-chain interactions is important for defending decentralized applications against vulnerabilities, assuring the secure transfer of assets among blockchains, and embracing trust and stability within the extensive blockchain ecosystem. The paper also examines different tools and techniques available in the literature for improving blockchain security, including techniques for vulnerability detection along with the surging impact of deep learning in vulnerability detection. This work presents beneficial insights into the prevailing state of blockchain security and provides a progressive prospect on the capability of intelligent, high-dimensional deep learning-based solutions to target future threats in the blockchain security space.

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF‐IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF‐IDF unigram model confirming 0.67 F1 score. The proposed multi‐ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.