差分隐私保证的求和问题的实例最优裁剪
讲座时间：2025-07-11 09:30讲座地点：清水河校区主楼B1-104
特邀专家： Wei Dong，长期从事差分隐私、数据安全与数据库理论研究，在SIGMOD、PODS、CCS、IEEE S&P、VLDB、NeurIPS等国际顶级会议发表多篇论文，成果多次获得最佳论文奖和SIGMOD博士论文奖Runner-up等重要荣誉。其研究涵盖差分隐私机制、联邦学习中的隐私保护、私有SQL处理等领域，并提出多种实例最优（Instance-optimal）数据发布技术。
讲座内容： Many differentially private mechanisms focus on worst-case error bounds, but instance-specific guarantees—where error depends on actual data values—can provide more meaningful accuracy. In the sum estimation problem, existing methods in the shuffle model often require multiple communication rounds to achieve instance-optimal accuracy. We propose a new approach that performs both clipping and sum estimation in a single round, significantly improving efficiency while maintaining strong accuracy guarantees. Our method also extends to high-dimensional sum estimation and sparse vector aggregation, making it a practical solution for privacy-preserving data analysis.
