Advanced Security and Privacy Techniques for Secure Big Data Query, Sharing and Processing

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The widespread application of information and communication technology has continuously promoted the explosive growth of data in various fields. It has been estimated that approximately 2.5 quintillion bytes of data are produced each day, and such data play a vital role in decision making, business planning, knowledge discovery, etc. Obviously, this situation has resulted in continuing interest in big data analytics. Nevertheless, while big data brings us many opportunities, it also introduces new challenges, especially security and privacy challenges. If we do not pay attention to big data security, false injected data would make big data driven applications useless. On the other hand, most valuable data are usually personal and sensitive, if we do not pay attention to big data privacy, data owners will have no confidence in sharing their data. Therefore, in order to adapt Canada to the big data era, big data security and privacy should never be an afterthought. Instead, Canada should prepare not only a new generation of data engineers skilled in big data, but also reliable and trustworthy platforms to ensure secure and privacy-preserving data capture, curation, storage, search, and sharing in big data era. The proposed research is envisioned to address security and privacy challenges in big data era, particularly considering the security and privacy threats in complex big data query, sharing, and processing, which have not yet been fully exploited in previously reported studies. The main objective of this proposed research is to investigate a set of advanced security and privacy techniques by using an interdisciplinary approach, i.e., combining cryptography, advanced data structures, and data mining techniques, to secure data query, sharing, and processing in various big data applications. In particular, this proposal will address significant technical challenges arising from "4V" (Volume, Velocity, Variety, and Veracity) characteristics of big data, in the following four thrusts: i) develop efficient and privacy-preserving similarity-based query techniques to balance utility, privacy, and efficiency in eHealthcare big data systems; ii) develop efficient and privacy-preserving "skyline variants" query techniques to fit various real scenarios' needs; iii) design privacy-preserving query techniques over encrypted graphs for preserving access pattern privacy; and iv) develop reliable, privacy-preserving, and access controllable frameworks to secure data sharing and processing in big data digital twin systems. This proposed research will draw intensively and extensively on the research expertise of the Principal Investigator, as well as the strong supports from the Faculty of Computer Science, University of New Brunswick. The cutting-edge research will generate new ideas and knowledge for the evolution of big data security, enable HQP training and provide new secure and privacy-preserving big data solutions for Canada.