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Title: Query Evaluation under Differential Privacy
Speaker: Wei Dong, Ph.D. Candidate, Department of Computer Science of Engineering, Hong Kong University of Science and Technology
Date: May 29, 2023
Time: 11:00am – 12:00nn

Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.


Differential privacy (DP) has garnered significant attention from both academia and industry due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of SQLs while maintaining DP has become the holy grail in privacy-preserving query release. However, there are two significant challenges. First, guaranteeing privacy in a relational database with multiple relations, foreign keys, and the join operator is challenging since individuals can make large and correlated contributions to the query results. Second, noise injection is essential for privacy protection, but traditional notions of optimality, such as instance optimality and worst-case optimality, are either unachievable or meaningless when evaluating relational queries under DP, further complicating the task of achieving an optimal privacy-utility trade-off. In this talk, I will give a selective overview of my recent research in addressing these challenges in SQL queries answering under DP.


Wei Dong
Ph.D. Candidate @ Department of Computer Science and Engineering, Hong Kong University of Science and Technology
Wei Dong is a final-year Ph.D. candidate in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. His general areas of interest include database theory and algorithms, data security and privacy, and statistics. His research has been recognized by the academic community and appeared in top conferences, such as SIGMOD, PODS, S&P, CCS, NeuIPS, and KDD. He received the Best Paper Award in SIGMOD 2022. He is also the receipt of HKUST Engineering PhD Research Excellence Award 2023, which is a distinguished honor granted to single Ph.D. student from the School of Engineering at HKUST.

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