📘Biography:
Dr. Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include data privacy and security, cloud computing, scalable data processing, data systems and related various research topics. He has published more than 300 papers in international journals and conferences. He received various awards such as IEEE TCSC Award for Excellence in Scalable Computing and Australia's Top Researchers. He has served as an Associate Editor for ACM Computing Surveys, IEEE TC, TCC and TSUSC. He is a MAE (Academia Europea), IEEE Fellow (IEEE Computer Society), AAIA Fellow, AIIA Fellow, and Chair of IEEE TCSC.

📄Abstract:
DP (Differential Privacy) mechanisms have been widely used in statistical privacy applications such as various search engines. The most kind of traditional DP mechanisms (e.g. Laplace, Gaussian, etc.) have unlimited output range. In real scenarios, most datasets have bounded output range, e.g. age [0-150]. Users would then need to use post-processing or truncated mechanisms to forcibly bound output distribution. However, these mechanisms would incur bias problem which has been a long-known DP challenge, resulting in various unfairness issues in subsequent applications. A tremendous amount of research has been done on analyzing this bias problem and its consequences, but no solutions can solve it fully. As the world-first solution to solve this long-known DP bias problem, this talk will present a new innovative DP mechanism named Composite DP. It will first illustrate this long-known bias problem, and then detail the rational of the new mechanism and its example noise functions as well as their implementation algorithms. All source codes are publicly available on Github for any deployment or verification.