Security and Privacy Issues and Challenges in Big Data Era

Dear Colleagues,

Due to the recent proliferation in digital solutions such as epidemic handling systems (EHSs), social networks (SNs), recommender systems, cyber-physical social systems (CPSSs), and the internet of things (IoT), a large amount of personal data is being collected and processed. These collected data often contain information about an individual’s identity (i.e., demographics), spatial-temporal activities, salary and disease information, social life activities, etc. On the one hand, these data are regarded as the oil of the economy because they can influence science and advance societies when processed with advanced data mining and analytics tools. On the other hand, the mishandling of these data can spark public criticism and anger if they are not processed with tight privacy protection. The COVID-19 pandemic has also shown that privacy and security are two major bottlenecks when it comes to handling personal data encompassing basic and sensitive information. Furthermore, making sense of data (e.g., drawing conclusions out of data) with privacy preservation is another longstanding challenge in academia and research. To strike the balance between utility and privacy, many studies have been proposed. Nevertheless, some technical challenges and open research gaps remain in the area of privacy-preserving computing for analytics and mining purposes that leverage big data. 

This Special Issue aims to present recent advances in tools, methods, techniques, prototypes, case studies, and technologies to improve privacy and security by leveraging traditional and AI technologies in the big data era. Topics of interest include, but are not limited to: 

• Privacy-preserving big data computing and processing;
• Privacy-preserving data publishing;
• Privacy-preserving data mining;
• Anonymization of big data;
• Differential privacy-based method to secure big data;
• Social network privacy preservation;
• Analytic techniques with privacy guarantees;
• Emerging privacy threats due to the adoption of social networks; 
• IoT privacy challenges and innovative solutions;
• Big data privacy and security;
• Cloud computing privacy issues and solutions;
• Encryption techniques to protect the contents of big data;
• Advance privacy protection techniques pertinent to the COVID era;
• Privacy issues in cyber-physical social systems (CPSSs);
• Case studies about people's perceptions of privacy in different regions;
• Emerging privacy issues due to digitization across the globe;
• Data-centric anonymization techniques to secure data sharing;
• Light-weight anonymization methods for resource-constrained IoT environments;
• Legal measures for privacy preservation in contact tracing methods;
• Privacy protection techniques for heterogeneous data formats;
• The data challenges posed by artificial intelligence in societal domains;
• Privacy preservation of AI-based systems such as federated learning;
• Privacy-enhancing techniques for big data-based smart healthcare applications;
• Privacy protection for heterogeneous data styles (images, text, tables, multimedia, transactional databases, trajectories, etc.).

Dr. Abdul Majeed
Dr. Xiaohan Zhang
Guest Editors

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