Data Privacy

The wide adoption of big data and data science technologies requires extensive collection of data from individuals and businesses. The research of data privacy is to design and deploy privacy-preserving data collection technologies, including but not limited to, (local) differentially privacy, secure multiparty computation, data anonymization, to enforce transparent privacy protection required by data privacy legislations such as GDPR and CCPA. The main challenge is to address the balance between privacy and utility, i.e., good quality of service.

Selected Publications:

  • Q. Ye, H. Hu, K. Huang, M. H. Au, and Q. Xue. “Stateful Switch: Optimized Time Series Release with Local Differential Privacy”. IEEE International Conference on Computer Communications (INFOCOM), 2023.
  • J. Duan, Q. Ye, and H. Hu. “Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space.” Proc. of the 38th IEEE International Conference on Data Engineering (ICDE ’22), Kuala Lumpur, Malaysia, May 2022.
  • Q. Xue, Q. Ye, H. Hu, Y. Zhu, and J. Wang. “DDRM: A Continual Frequency Estimation Mechanism with Local Differential Privacy.” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.
  • Q. Ye, H. Hu, X. Meng, H. Zheng, K. Huang, C. Fang, and J. Shi. “PrivKVM*: Revisiting Key-Value Statistics Estimation with Local Differential Privacy.” IEEE Transactions on Dependable and Secure Computing (TDSC), 2021.
  • Q. Ye, H. Hu, N. Li, X. Meng, H. Zheng, H. Yan. “Beyond Value Perturbation: Differential Privacy in the Temporal Setting.” Proc. of IEEE International Conference on Computer Communications (INFOCOM’21), Virtual, May 2021.
  • R. Du, Q. Ye, Y. Fu, and H. Hu. “Collecting High-Dimensional and Correlation-Constrained Data with Local Differential Privacy.” Proc. of 18th IEEE International Conference on Sensing, Communication and Networking (SECON), Virtual, 2021.
  • Q. Ye, H. Hu, M. H. Au, X. Meng, X. Xiao. Towards Locally Differentially Private Generic Graph Metric Estimation. Proc. of the 36th IEEE International Conference on Data Engineering (ICDE ’20), Dallas, USA, Apr. 2020, pp 1922-1925.
  • Q. Ye, H. Hu, X. Meng, and H. Zheng. “PrivKV: Key-Value Data Collection with Local Differential Privacy.” Proc. of 40th IEEE Symposium on Security and Privacy (SP’19), San Francisco, USA, May 2019.
  • L. Yao, X. Wang, X. Wang, H. Hu, and G. Wu. “Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model.” Proc. of 20th IEEE International Conference on Mobile Data Management (MDM’19), Hong Kong SAR, China. (Best Paper Award)
  • C. Liu, S. Zhou, H. Hu, Y. Tang, J. Guan, and Y. Ma. “CPP: Towards Comprehensive Privacy Preserving for Query Processing in Information Networks.” Information Sciences, Volume 467, October 2018, pages 296-311.
  • H. Li, H. Hu, J. Xu. “Nearby Friend Alert: Location Anonymity in Mobile Geo-Social Networks”. IEEE Pervasive Computing, 12(4): 62-70, 2013.
  • H. Hu, J. Xu, C. Ren, and B. Choi. “Processing Private Queries over Untrusted Data Cloud through Privacy Homomorphism.” Proc. of the 27th IEEE International Conference on Data Engineering (ICDE ’11), pp. 601 – 612.
  • H. Hu, J. Xu, S. T. On, J. Du, and K. Y. Ng. “Privacy-Aware Location Data Publishing”. ACM Transactions on Database Systems (TODS), 35(3), July 2010.
  • H. Hu and J. Xu. “2PASS: Bandwidth-Optimized Location Cloaking for Anonymous Location-Based Services.” IEEE Transactions on Parallel and Distributed Systems (TPDS), 21(10): 1458-1472, October 2010.
  • H. Hu, J. Xu and D. L. Lee. “PAM: An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Objects.” IEEE Transactions on Data and Knowledge Engineering (TKDE), 22(3): 404-419, March 2010.
  • H. Hu and J. Xu. “Non-Exposure Location Anonymity.” Proc. the 25th IEEE Int. Conf. on Data Engineering (ICDE ’09), Shanghai, China, pp. 1120-1131.

Externally Funded Projects:

  • Protecting Metadata Privacy for Mobile Crowdsensing Using Oblivious RAM (RGC/GRF, 15238116, 2017-2020, HK$ 482,605)
  • Privacy-Preserving Mobile User Behavior Statistics Collection (Huawei Innovation Research Program, 2017-2018, US$ 30,000)
  • Privacy Preservation Techniques for Query Processing in Big Data 大数据查询处理的隐私保护技术 (Co-PI: Joint Funds of National Natural Science Foundation of China (Key Program) 国家自然科学基金联合基金重点支持项目合作单位负责人, U1636205, 2017-2020, CNY 2,520,000, PI: Prof. Zhou Shuigeng)
  • Mutual Privacy Protection on Private Queries over Large-Scale Private Data 海量数据查询中的双向隐私保护机制研究 (National Natural Science Foundation of China 国家自然科学基金面上项目, 61572413, 2016-2019, CNY 630,000)
  • Incognito Browsing of Spatial-Temporal Data Using Computational Private Information Retrieval (RGC/GRF, 12200914, 2014-2017, HK$ 692,894)


  • Q. Ye and H. Hu. Method and apparatus for collecting key-value pair data. US Patent Application 17/108,780, Mar 2021.
  • 叶青青,胡海波.“键值对数据的收集方法和装置”,中国专利发明(China Patent),申请号201811161746.5, Sept 2018.
  • H. Hu, Z. Chen, and J. Yu. “Privacy-Preserving Large-Scale Location Monitoring.” US Patent No. 9,756,461, Sept 2017.
  • J. Xu and H. Hu. “A System and Method for Providing Proximity Information.” US Patent No. 9,351,116 B2, May 2016.