Dr. Qingqing Ye

Assistant Professor
Department of Electrical and Electronic Engineering
The Hong Kong Polytechnic University
Email: [email protected]

Research Interests
  • Differential privacy
  • Adversarial machine learning

About Me

I received my PhD degree from Renmin University of China in 2020, and joined the Hong Kong Polytechnic University as a Research Assistant Professor since then. In 2022, I was promoted as an Assistant Professor. My research interests include data privacy and security, and adversarial machine learning. You can refer to our research lab ASTAPLE for more information.

I am looking for PhD students (Spring/Fall 2024 Admission), Research Assistants, and Postdoctoral Fellows in the field of differential privacy, and adversarial machine learning. If you are interested, please send me your CV at [email protected].

News
  • Sep. 2023: Our paper “Collecting Multi-type and Correlation-Constrained Streaming Sensor Data with Local Differential Privacy” is accepted by ACM Transactions on Sensor Networks (TOSN).
  • Aug. 2023: Our paper “TED+: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database” is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).
  • Aug. 2023: A research project entitled “本地化差分隐私攻防之数据重构攻击研究” is awarded by NSFC (面上项目) with CNY 500,000 (2024.01-2027.12).
  • Jun. 2023: A General Research Fund (GRF) entitled “Small Leaks Sink Great Ships: Data Recovery Attacks and Defense in Local Differential Privacy” is awarded by Research Grant Council, HKSAR with HK$ 1,096,927 (2024.01-2026.12).
  • Jun. 2023: Our paper “PUTS: Privacy-Preserving and Utility-Enhancing Framework for Trajectory Synthesization” is accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).
  • Jun. 2023: Our paper “Collaborative Sampling for Partial Multi-dimensional Value Collection under Local Differential Privacy” is accepted by IEEE Transactions on Information Forensics and Security (TIFS).
  • May 2023: Our paper “Trajectory Data Collection with Local Differential Privacy” is accepted by International Conference on Very Large Databases (VLDB 2023).
  • Apr. 2023: Our paper “3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning” is accepted by IEEE Symposium on Security and Privacy (S&P 2023).
  • Feb. 2023: Our paper “Differential Aggregation against General Colluding Attackers” is accepted by IEEE International Conference on Data Engineering (ICDE 2023).
  • Jan. 2023: Our paper “Synthesizing Realistic Trajectory Data with Differential Privacy” is accepted by IEEE Transactions on Intelligent Transportation Systems (TITS).

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