题目：《Collecting and Analyzing Multidimensional Data with Local Differential Privacy》
地址：腾讯会议965 228 7322
摘 要: Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this report, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks.
主讲人简介：王宁，中国海洋大学讲师，于2017年10月于东北大学获得博士学位，于2014.12-2015.12赴新加坡南洋理工大学进行为期一年的联合培养，并于2018 .5 月至 2018.8 月在新加坡国立大学担任研究助理工作。目前从事数据安全中差分隐私模型相关研究，主要围绕移动终端个体用户的数据安全问题设计隐私保护方案。现主持国家自然科学青年基金、博士后面上项目等3项。近年在国内外会议和期刊上以第一作者身份发表关于隐私保护方面的论文7篇，其中3篇以长文的形式被计算机学会推荐的A类会议录用，4篇被SCI收录。