Online user interactions produce privacy-sensitive data, necessitating data governance frameworks that respect user-defined privacy preferences. Nevertheless, the centralized storage and management of such preferences encounter significant interoperability challenges across different domains. By combining on-chain and off-chain storage paradigms, hybrid-storage blockchains have developed into a reliable infrastructure. Their value lies not only in ensuring secure data storage, but also in providing a practical foundation for data governance, especially in complex and cross-domain environments. In this paper, we propose LiveIndex, an index-driven hybrid-storage blockchain framework for cross-domain data governance. First, we design an on-chain data index based on distributed chameleon hash (DCH) to enable efficient off-chain data governance. This index incorporates essential elements, including metadata tags, data addresses, privacy ciphertexts, and group signatures. Second, we employ asymmetric searchable encryption (ASE) to protect privacy by converting analysis policies into search trapdoors and embedding privacy attributes into ciphertexts, thereby supporting compliant and secure data analysis. Third, we integrate proxy re-encryption (PRE) to strengthen control over cross-domain data, using re-encryption keys to ensure secure data authorization. Finally, we implement and evaluate LiveIndex on public datasets. The results show time overheads ranging from milliseconds to microseconds, demonstrating the practicality and efficiency in real-world scenarios.

ABSTRACT Access control is a critical security measure to ensure that sensitive information and resources are accessed only by authorized users. However, attribute‐based access control in the big data environment faces challenges such as a large number of entity attributes, poor availability, and difficulty in manual labeling. In this paper, we focus on the problem of mining and optimizing security attributes of unstructured data resources and propose a method for mining security attributes of unstructured textual data based on multi‐model collaboration. First, we utilize unsupervised methods to extract candidate attributes from textual resources, and then weight the results of multiple methods using rough set theory to obtain the optimal result. Second, considering various factors including the text itself and the candidate attributes, we construct a feature vector consisting of 45 categories to represent the candidate attributes. Third, we employ a multi‐model voting method to collaboratively train the attribute mining model and obtain the security attributes of textual resources. Finally, based on HowNet, we optimize the security attributes to achieve automated and intelligent mining of access control data resource security attributes, providing an attribute foundation for precise access control. The experiments indicate that the attribute mining precision rate of the method proposed in this paper can reach up to 92.36%, F1‐score can reach up to 82.51%. The attribute scale can be compressed to 69.59% of its original size after optimization. This method has a greater advantage over other methods and can provide attribute support for access control of large data resources.

In the current era of big data, the advancement in data generation and management has created an avenue for decision makers to utilize these huge data collected from many data-driven application domains for different purposes. Big data science enables application developers and data scientists to utilize these big data, to learn more about the data, and then to explore and model hidden features for analysis purposes. In this paper, we present a data science solution to support social and economic analysis. Our solution makes good use of data mining techniques to cluster similar data, analyze time series, find frequent patterns, reveal interesting associations, and visualize these relationships. We evaluate our solution with two sets of real-life employment data. Our solution utilizes employment data to support social and economic analysis. It enables users to explore and discover implicit, previously unknown information and useful knowledge from the data. This, in turn, can enable the decision makers to take appropriate actions for social good and/or economic benefits. As an example, it reveals to job seekers some interesting characteristics of different data-related jobs, which helps them to find jobs that match better with their needs and profiles. As another example, it also reveals to social scientists and economists impacts of COVID-19 to the job markets, which helps them to get a better understanding of social and economic situations at the COVID-19 pandemic era and plan for the post-pandemic era.

In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. These big data can be of different levels of veracity in the sense that some of them are precise while some others are imprecise and uncertain. Embedded in these big data are useful information and valuable knowledge to be discovered. An example of these big data is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data-via data science techniques such as machine learning, data mining, and online analytical processing (OLAP)-helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a machine learning and big data analytic tool for processing and analyzing COVID-19 epidemiological data. Specifically, the tool makes good use of taxonomy and OLAP to generalize some specific attributes into some generalized attributes for effective big data analytics. Instead of ignoring unknown or unstated values of some attributes, the tool provides users with flexibility of including or excluding these values, depending on their preference and applications. Moreover, the tool discovers frequent patterns and their related patterns, which help reveal some useful knowledge such as absolute and relative frequency of the patterns. Furthermore, the tool learns from the patterns discovered from historical data and predicts useful information such as clinical outcomes for future data. As such, the tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for machine learning and analytics of big epidemiological data, it would be applicable to machine learning and analytics of big data in many other real-life applications and services.

In the current era of big data, a huge amount of data has been generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. As “a picture is worth a thousand words”, having methods to visualize and visually analyze these big data makes it easily to comprehend the data and the discovered knowledge. In this paper, we present a big data visualization and visual analytics tool for visualizing and analyzing COVID-19 epidemiological data. The tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for visualization and visual analytics of epidemiological data, it is applicable to visualization and visual analytics of big data from many other real-life applications and services.