Correlated Differential Privacy: Feature Selection in Machine Learning
University of Technology Sydney（悉尼科技大学）；China University of Geosciences（中国地质大学）；Zhongnan University of Economics and Law（中南财经政法大学）；RMIT University（皇家墨尔本理工大学）
Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, we propose a correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks. %The key to the proposed scheme is to describe the data correlation and select features which leads to less data correlation across the whole dataset. The proposed scheme involves five steps with the goal of managing the extent of data correlation, preserving the privacy, and supporting accuracy in the prediction results. In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover, the privacy issue of data correlation in learning is guaranteed. The proposed method can be widely used in machine learning algorithms which provide services in industrial areas. Experiments show that the proposed scheme can produce better prediction results with machine learning tasks and fewer mean square errors for data queries compared to existing schemes.
Purdue University（普渡大学）；Department of Statistics, Purdue University（普渡大学统计系）；Mitchell E. Daniels, Jr. School of Business, Purdue University（普渡大学米切尔·E·丹尼尔斯 Jr.商学院）
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Technical University of Munich (TUM)（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学医院）；Imperial College London（伦敦帝国理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；deepc GmbH（deepc有限公司）
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Department of Information Engineering, The Chinese University of Hong Kong（信息工程系，香港中文大学）；School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society（科学与工程学院，深圳人工智能与机器人研究院）；The Chinese University of Hong Kong（香港中文大学）
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；School of Science and Engineering（科学与工程学院）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；ZJU-UIUC Institute（浙大-伊利诺伊大学联合学院）；Zhejiang University（浙江大学）；School of Management（管理学院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来智能网络研究院（FNii-Shenzhen））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network（深圳群智赋能低碳能源网络重点实验室）；CSIJRI Joint Research Centre on Smart Energy Storage（CSIJRI联合智能储能研究中心）
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University（深圳大学广东省人工智能与数字经济实验室（SZ））；School of Computing, Faculty of Science and Engineering, Macquarie University（麦考瑞大学科学与工程学院计算学院）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与网络空间研究院）；School of Computing Science, Simon Fraser University（西蒙菲莎大学计算科学学院）
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
Department of Information Engineering, The Chinese University of Hong Kong（信息工程系，香港中文大学）；School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society（科学与工程学院，深圳人工智能与机器人社会研究院）
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
School of ECE, Ben-Gurion University of the Negev（本·加里昂大学电子工程学院）；Department of ECE, National Yang-Ming Chiao-Tung University（National Yang-Ming Chiao-Tung大学电子工程系）
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets
University of Bayreuth（拜罗伊特大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）
Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise
School of Data and Sciences（数据与科学学院）；BRAC University（布拉克大学）；Electrical and Computer Engineering（电气与计算机工程）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Electrical and Electronic Engineering（电气与电子工程）；Bangladesh University Engineering and Technology（孟加拉工程与技术大学）
University of Michigan（密歇根大学）；University of Pennsylvania（宾夕法尼亚大学）；Google Research（谷歌研究院）
能够高效准确地选择具有差分隐私的顶级$k$元素是各种隐私数据分析任务的组成部分。在本文中，我们提出了oneshot拉普拉斯机制，它概括了著名的报告噪声最大的机制，以报告嘈杂的顶部$k$元素。我们发现，oneshot拉普拉斯机制的噪音水平为$\widetilde{O}(\sqrt{k}/\eps)$近似差分私人。与之前运行Report Noisy Max $k$次的剥离方法相比，oneshot Laplace机制仅添加噪声并计算顶部$k$元素一次，因此对于大型$k$更有效。此外，我们的隐私证明依赖于一种新的耦合技术，绕过使用的组合定理。最后，我们提出了一个新的应用程序，有效的顶部$k$选择的经典问题，从成对比较排名。
