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:

  • S. Wang, Y. Zheng, X. Jia, and H. Hu. “PrivAGM: Secure Construction of Differentially Private Directed Attributed Graph Models on Decentralized Social Graphs.” Proceedings of the VLDB Endowment (PVLDB), London, United Kingdom, September 1-5, 2025.
  • R. Du, Q. Ye, Y. Fu, and H. Hu. “Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services.” Proceedings of the VLDB Endowment (PVLDB), London, United Kingdom, September 1-5, 2025.
  • S. Zhang, H. Hu, Q. Ye, and J. Xu. “PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy.” 31st SIGKDD Conference on Knowledge Discovery and Data Mining, Toronto, Canada, August 2025.
  • Y. Zhang, Q. Ye, and H. Hu. “Federated Heavy Hitter Analytics with Local Differential Privacy.” ACM SIGMOD International Conference on Management of Data, Berlin, Germany, June 2025.
  • R. Hou, Q. Ye, X. Ran, S. Zhang, and H. Hu. “PrivIM: Differentially Private Graph Neural Networks for Influence Maximization.” Proc. of the 41th IEEE International Conference on Data Engineering (ICDE ’25), Hong Kong, China, May 2025.
  • R. Du, Q. Ye, Y. Xiao, L. Yu, Y. Fu, and H. Hu. “Dual Utilization of Perturbation for Stream Data Publication under Local Differential Privacy.” Proc. of the 41th IEEE International Conference on Data Engineering (ICDE ’25), Hong Kong, China, May 2025.
  • Y. Mao, Q. Ye, R. Du, Q. Wang, K. Huang, and H. Hu. “Multi-class Item Mining under Local Differential Privacy.” Proc. of the 41th IEEE International Conference on Data Engineering (ICDE ’25), Hong Kong, China, May 2025.
  • S. Zhang, Q. Ye, H. Hu, J. Xu. “AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model.” Proc. of the 41th IEEE International Conference on Data Engineering (ICDE ’25), Hong Kong, China, May 2025.
  • S. Zhang, Q. Ye, and H. Hu. “Structure-Preference Enabled Graph Embedding Generation under Differential Privacy.” Proc. of the 41th IEEE International Conference on Data Engineering (ICDE ’25), Hong Kong, China, May 2025.
  • J. Duan, H. Hu, Q. Ye, and X. Sun. “Analyzing and Optimizing Perturbation of DP-SGD Geometrically.” Proc. of the 41th IEEE International Conference on Data Engineering (ICDE ’25), Hong Kong, China, May 2025.
  • J. Duan, Q. Ye, H. Hu, and X. Sun. “Analyzing and Enhancing LDP Perturbation Mechanisms in Federated Learning.” IEEE Transactions on Knowledge and Data Engineering (TKDE), accepted to appear, 2025.
  • L. Yao, G. Wu, H. Hu, G. Wu, and S. Geng. “η-Inference: A Data-Aware and High-Utility Privacy Model for Relational Data Publishing.” IEEE Transactions on Dependable and Secure Computing (TDSC), accepted to appear, 2025.
  • R. Du, Q. Ye, Y. Fu, H. Hu, K. Huang. “Top-k Discovery under Local Differential Privacy: An Adaptive Sampling Approach.” IEEE Transactions on Dependable and Secure Computing (TDSC), Volume 22, Issue 2, Mar.-Apr. 2025, pp. 1763 – 1780.
  • L. Wang, Q. Ye, H. Hu, and X. Meng. “PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy.” Proceedings of the VLDB Volume 17 (PVLDB ’24), Guangzhou, China, August 2024.
  • J. Fu, Q. Ye, H. Hu, Z. Chen, L. Wang, K. Wang, and X. Ran. “DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release.” Proceedings of the VLDB Volume 17 (PVLDB ’24), Guangzhou, China, August 2024.
  • X. Ran, Q. Ye, H. Hu, X. Huang, J. Xu, and J. Fu. “Differentially Private Graph Neural Networks for Link Prediction.” Proc. of the 40th IEEE International Conference on Data Engineering (ICDE ’24), Utrecht, Netherlands, May 2024.
  • Y. Mao, Q. Ye, H. Hu, Q. Wang, and K. Huang. “PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy.” Proc. of the 40th IEEE International Conference on Data Engineering (ICDE ’24), Utrecht, Netherlands, May 2024.
  • X. Sun, Q. Ye, H. Hu, J. Duan, T. Wo, J. Xu, and R. Yang. “LDPRecover: Recovering Frequencies from Poisoning Attacks against Local Differential Privacy.” Proc. of the 40th IEEE International Conference on Data Engineering (ICDE ’24), Utrecht, Netherlands, May 2024.
  • X. Sun, Q. Ye, H. Hu, J. Duan, Q. Xue, T. Wo, J. Xu, and W. Zhang. “Generating Location Traces with Semantic-Constrained Local Differential Privacy.” IEEE Transactions on Information Forensics and Security (TIFS), Volume 19, 9850-9865, October 2024.
  • J. Cai, Q. Ye, H. Hu, X. Liu, Y. Fu. “Boosting Accuracy of Differentially Private Continuous Data Release for Federated Learning.” IEEE Transactions on Information Forensics and Security (TIFS), Volume 19, 10287-10301, October 2024.
  • J. Duan, Q. Ye, H. Hu, and X. Sun. “LDPTube: Theoretical Utility Benchmark and Enhancement for LDP Mechanisms in High-dimensional Space.” IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume: 36, Issue: 8, August 2024.
  • X. Sun, Q. Ye, H. Hu, J. Duan, Q. Xue, T. Wo, and J. Xu. “PUTS: Privacy-Preserving and Utility-Enhancing Framework for Trajectory Synthesization.” IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume: 36, Issue: 1, January 2024, 296 – 310.
  • L. Wang, Q. Ye, H. Hu, X Meng. “EPS: Privacy Preserving Set-Valued Data Analysis in the Shuffle Model.” IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume: 36, Issue: 11, November 2024.
  • L. Yao, X. Wang, H. Hu, and G. Wu. “A Utility-aware Anonymization Model for Multiple Sensitive Attributes Based on Association Concealment.” IEEE Transactions on Dependable and Secure Computing (TDSC), Volume: 21, Issue: 4, July-Aug. 2024, pp. 2045-2056.
  • Y. Mao, Q. Ye, Q. Wang, H. Hu. “Utility-Aware Time Series Data Release with Anomalies under TLDP”. IEEE Transactions on Mobile Computing (TMC), Volume: 23, Issue: 6, June 2024, 7135 – 7147.
  • Y. Zhang, Q. Ye, R. Che, H. Hu, and Q. Han. “Trajectory Data Collection with Local Differential Privacy.” Proceedings of the VLDB Volume 16 (PVLDB ’23), Vancouver, Canada, August 2023.
  • Q. Qian, Q. Ye, H. Hu, K. Huang, T. Chan, and J. Li. “Collaborative Sampling for Partial Multi-dimensional Value Collection under Local Differential Privacy.” IEEE Transactions on Information Forensics & Security (TIFS), accepted to appear, 2023.
  • 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.
  • R. Du, Q. Ye, Y. Fu, H. Hu, J. Li, C. Fang, and J. Shi. “Differential Aggregation against General Colluding Attackers”. Proc. of the 39th IEEE International Conference on Data Engineering (ICDE ’23), Anaheim, CA, USA, April 2023.
  • Y. Yan, Q. Ye, H. Hu, R. Chen, Q. Han, and L. Wang. “Towards Defending Against Byzantine LDP Amplified Gain Attacks.” 28th International Conference on Database Systems for Advanced Applications (DASFAA), Tianjin, China, Apr 2023.
  • 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), Volume: 35, Issue: 7, July 2023, pp. 6784-6797.
  • 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), Volume: 20, Issue: 1, Jan., pp 17-35, 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. Ye, H. Hu, M. H. Au, X. Meng, X. Xiao. “LF-GDPR: A Framework for Estimating Graph Metrics with Local Differential Privacy.” IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 34, Issue 10, pages 4905 – 4920, 2022.
  • K. Huang, H. Hu, S. Zhou, J. Guan, Q. Ye, and X. Zhou. “Privacy and Efficiency Guaranteed Social Subgraph Matching.” The VLDB Journal (VLDBJ), Volume 31, pages 581–602, 2022.
  • 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)

Patents:

  • Q. Ye and H. Hu. Method and apparatus for collecting key-value pair data. US Patent No. 11,615,099 B2, Mar 2023.
  • 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)，授权公告号CN 110968612 B, July 18, 2023.
  • 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.