A canonical noise distribution (CND) is an additive mechanism designed to satisfy $f$-differential privacy ($f$-DP), without any wasted privacy budget. $f$-DP is a hypothesis testing-based formulation of privacy phrased in terms of tradeoff functions, which captures the difficulty of a hypothesis test. In this paper, we consider the existence and construction of both log-concave CNDs and multivariate CNDs. Log-concave distributions are important to ensure that higher outputs of the mechanism correspond to higher input values, whereas multivariate noise distributions are important to ensure that a joint release of multiple outputs has a tight privacy characterization. We show that the existence and construction of CNDs for both types of problems is related to whether the tradeoff function can be decomposed by functional composition (related to group privacy) or mechanism composition. In particular, we show that pure $ε$-DP cannot be decomposed in either way and that there is neither a log-concave CND nor any multivariate CND for $ε$-DP. On the other hand, we show that Gaussian-DP, $(0,δ)$-DP, and Laplace-DP each have both log-concave and multivariate CNDs.
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
Harvard University（哈佛大学）
我们比较了纯$ε$ -差分隐私[Dwork et al. TCC 2006]和近似$(ε,δ)$-差分隐私[Dwork et al. Eurocrypt 2006]下私人学习[Kasiviswanathan et al. 2008]和消毒[Blum et al. 2008]的样本复杂度。我们表明，这些任务的样本复杂度下近似差分隐私可以显着低于纯差分隐私。我们定义了一个家庭的优化问题，我们称之为准凹承诺问题，概括了我们考虑的一些任务。我们观察到，一个准凹的承诺问题可以私下近似使用一个较小的实例的一个准凹的承诺问题的解决方案。这使我们能够构建一个有效的递归算法解决这些问题的私人。具体来说，我们构造私人学习点函数，阈值函数，和轴对齐的矩形在高维。类似地，我们构造点函数和阈值函数的消毒器。我们还研究了标签私人学习者的样本复杂性，这是一种放松的私人学习，学习者只需要保护样本中标签的隐私。我们表明，VC维度完全表征了这样的学习者的样本复杂性，即，学习的样本复杂性与标签隐私是平等的（常数）学习没有隐私。
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
Southern University of Science and Technology（南方科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；South China University of Technology（华南理工大学）；Jilin University（吉林大学）
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
ETH Zurich（苏黎世联邦理工学院）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学习系统中心）；Amazon（亚马逊）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Washington（华盛顿大学）
$(ε, δ)$-Differentially Private Partial Least Squares Regression
Software Competence Center Hagenberg (SCCH) GmbH（哈根伯格软件能力中心有限公司）；Faculty of Computer Science and Electrical Engineering（计算机科学与电气工程学院）；University of Rostock（罗斯托克大学）
Private Mean Estimation with Person-Level Differential Privacy
Khoury College of Computer Sciences, Northeastern University（东北大学科里计算机学院）；Cheriton School of Computer Science, University of Waterloo（滑铁卢大学切里顿计算机科学学院）；Vector Institute（向量研究所）；Machine Learning Department, Carnegie Mellon University（卡内基梅隆大学机器学习系）
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?
Inria（法国国家信息与自动化研究所）；École Polytechnique (IPP)（巴黎综合理工学院（巴黎理工学院））；Femto-ST Institute（弗朗什-孔泰电子、力学、热学与光学科学技术研究所）；Univ. Bourg. Franche-Comté（勃艮第-弗朗什-孔泰大学）；UBFC（勃艮第-弗朗什-孔泰大学联盟）；CNRS（法国国家科学研究中心）；Orange Applications For Business（Orange商业应用公司）；Orange Labs.（奥兰治实验室）；Antonine University Hadat-Baabda（哈达特-巴卜达安东尼大学）；National University of Singapore（新加坡国立大学）
An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
Computer Science and Mathematics Division, Oak Ridge National Laboratory（橡树岭国家实验室计算机科学与数学 division）；Computational Science and Engineering Division, Oak Ridge National Laboratory（橡树岭国家实验室计算科学与工程 division）；HPC Department, Cineca（Cineca 高性能计算部）
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）
