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Background: The rapid development of modern technologies renders a convenient and efficient solution to implement Electronic Health Records (EHRs) systems. The rapid growth of healthcare data has a distinctive attribute of digital transformations. The big datasets of healthcare, their complexity and their dynamic nature have posed severe challenges associated with the analysis, pre-processing, privacy, security, storage, usability and data exchange. Material and Methods: We have performed the Systematic Literature Review (SLR) and followed the Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. SLR refers to the methodology that discovers, analyses and accesses recent research literature related to the subject field. The research papers were searched from academic repositories like IEEE, WOS, Scopus and PubMed for the previous five years on March 2023. Results: The designed search string provides 199 research articles in total. We filter the research articles based on inclusion-exclusion strategies and quality assessment metrics. Six main criteria for research inclusion-exclusion for SLR are formulated. These works of literature insight into 1) the issues associated with interoperability and security of EHRs by using the Blockchain (BC) technology, 2) different frameworks and tools to improve privacy and security in the healthcare domain, 3) the open issues of using BC technology in the electronic healthcare domain, 4) the standardized ways to store EHRs, 5) various ways to handle the big data using the BC systems and 6) the usage of Federated Learning (FL) to preserve the privacy of EHRs in the healthcare domain. We acquired 46 research articles based on the criteria (inclusion-exclusion) that investigate the above-mentioned issues. Conclusion: The SLR will serve as the state-of-the-art (SOTA) for future researchers in the field of BC in healthcare. Additionally, the paper provides insights to the new researchers to revolutionize the healthcare domain by adopting the latest digitalized technologies. The proposed study identified various reflections. It analyzed the architectural mechanism that supports the security and interoperability of EHRs. Secondly, the study described different tools and frameworks to improve the privacy and security of EHRs using the BC. Thirdly, the open issues of storing and preserving the EHRs using BC in the healthcare system were determined. Fourth, it analyzed and provided a detailed view of using standardized ways for storing and handling big data by using the BC system. Lastly, the usage of FL to preserve the privacy of EHRs was analyzed.

With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users’ personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users’ privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

Heterogeneous graph representation learning is to learn effective representations for nodes or (sub)graphs, which preserve node attributes and structural information. However, it is challenging to design a representation learning method for heterogeneous information networks (HINs) due to their diversity. Most of the existing HIN-oriented learning methods define a series of meta-paths. Then, they aggregate the representations learned from different meta-paths in the same hidden space. These methods do not consider semantic differences of different meta-paths, which leads to semantic confusion. And further affects the effectiveness of the learned representation. Given these issues, we introduce a Semantic Aware HIN Representation learning Network (SAHRN), which takes into account the semantics of different meta-paths. We mitigate the problem of semantic confusion by projecting nodes’ features into different hidden spaces separately according to different meta-paths. To further expand the scope of aggregation and enrich the aggregated information, we also design various variants of our model by adding layer aggregation. Extensive experiments on three standard HIN datasets show that SAHRN achieves consistent improvements compared to state-of-the-art graph representation learning methods. The experiments and analyses on each component of the model show the effectiveness of the proposed method. The source code is available on https://github.com/pingpingand/SAHRN.

The traditional food supervision system has problems such as lack of industry chain and data fragmentation, which has caused the phenomenon of field regulation to be uncomprehensive in the existing regulatory system and the disposal of response lag. Combining the characteristics of blockchain such as distribution, transparent, and collegiality with the actual needs of regional autonomy, we propose the hierarchical multi-domain blockchain (HMDBC) network structure and the secondary-check mechanism, which can support timely correction and replacement of the malicious supervision nodes by regional nodes co-governance, auxiliary verification of supervision nodes, and arbitration of superior regions. In order to optimize the supervision nodes election, we proposed fuzzy comprehensive evaluation model of credibility, which can be used to objectively and fairly evaluate the comprehensive reputation of each node in that region by considering various influencing factors of node performance indicators. Furthermore, we designed the data block structure model, which can support the supervisor node replacement. Our system can perform automatic food quality detection and warning of substandard food in the entire industrial chain, with the use of smart contracts combined with the food industry standards. Finally, comparing the complexity of the traditional blockchain system with PBFT consortium, our system is more secure, with lower broadcast complexity.

A collective action that considerably affects government management and public security, e.g., a mass demonstration, usually experiences a long development period, originating from small and uncertain variations called weak signals on social media. Researchers generally identify collective action by small changes in communication frequency, emerging key words, and sentiment. However, most studies only consider the present environment, which may not evolve into a collective action, or conduct a short-term prediction in which significant damage is already done when the collective action is identified. This paper proposes a predictive framework to identify potential collective actions, considering the future evolution as well as the present situation, and providing a reference for early decision making. In the framework, a future sign to describe events is improved and the enhanced gray system theory is used to predict the evolution of a future sign. Mentions of events surrounding the Arab Spring-using over 300000 different open-content Web sources crawled from social media in seven different languages-are analyzed, which suggests that the predictive framework can more precisely identify the weak signals of collective actions.