Differential Privacy Learning in Regression and Clustering

报告人：孔令臣

报告地点：惟真楼523教室

报告时间：2023年12月01日星期五10:10-11:10

邀请人：郑术蓉

报告摘要：

Statistical machine learning technology has rapidly developed and made significant breakthroughs in application fields. However, an attacker's intrusion can easily disrupt the learning process, leading to the system becoming unreliable. For example, in the field of precision medicine, attacks can lead to serious side effects in patients' treatment plans and leak their personal information. Trustworthy machine learning has gradually become a new and popular research direction in artificial intelligence, committed to making machine learning and trustworthy, including security, robustness, privacy, fairness, and interpretability. At present, privacy-preserving is gradually receiving attention and attention from experts and scholars in fields such as machine learning and statistics. Considering that regression and clustering are the fundamental positions of machine learning methods, this report will briefly outline the research progress of differential privacy techniques in regression and clustering, and introduce our new noise mechanism proposed in distributed learning to achieve privacy protection.

主讲人简介：

孔令臣，教授，博士生导师，中国运筹学会数学规划分会理事长，北京交通大学数学与统计学院副院长。主要从事对称锥互补问题和最优化、稀疏优化、低秩矩阵优化、高维数据聚类、矩阵回归、统计优化与学习、医学成像等方面的研究。在《Mathematical Programming》《SIAM Journal on Optimization》《IEEE Transactions on Pattern Analysis and Machine Intelligence》《IEEE Transactions on Signal Processing》《Technometrics》《Statistica Sinica》《Electronic Journal of Statistics》等期刊发表论文60余篇。主持国家自然科学基金面上项目“高维稳健隐私回归的优化模型理论与算法研究”“高维聚类的结构矩阵优化理论与算法”、“高维约束矩阵回归的优化理论与算法”、“矩阵秩极小问题的松弛理论与算法研究”和专项项目“统计优化与人工智能天元数学交流项目”等, 参与重点项目“大规模稀疏优化问题的理论与算法”以及973课题等。曾获中国运筹学会青年奖，教育部自然科学二等奖和北京市高等教育教学成果一等奖等。