Collaborative Research: SaTC: CORE: Small: Differentially Private Data Synthesis: Practical Algorithms and Statistical Foundations

协作研究：SaTC：核心：小型：差分隐私数据合成：实用算法和统计基础

• 批准号: 2247795
• 负责人: Guang Cheng
• 金额: $ 30万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247795&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

Data collected by organizations and agencies are a key resource in today’s information age and fuel a significant part of today's economy. However, the disclosure of those data poses serious threats to individual privacy. One important approach to using data while protecting privacy is differential private data synthesis (DPDS). That is, given as input a private dataset, one uses a differentially private algorithm to generate synthetic datasets that are “similar” to the input dataset. While DPDS has received much attention in recent years, our understanding on this topic remains limited. This project takes a multi-disciplinary approach to advance our scientific understanding as well as improve practice techniques for DPDS. More specifically, this project’s novelties are as follows. First, it systematically explores the design space in marginal-based DPDS algorithms that have been proven to be effective in NIST competitions on DPDS, while also taking insights from data synthesis techniques developed in similar fields (often not satisfying DP). Second, it develops statistical theories that both are motivated by the empirical performances of DPDS algorithms, and guide the empirical research of these algorithms. The project’s broader significance and importance are as follows. We are in the information economy. Data of all kinds, such as online interaction, medical sensor data, genomic data, and location data are being collected. Practical techniques that enable use of these data while protecting individual privacy are crucially needed and will greatly enhance the value of such data. Users will gain from increased control of their private information, and society as a whole will benefit from deriving maximal benefit from aggregated data. PIs plan to jointly develop and teach a graduate-level course on synthetic data based on the existing research in this area as well as research results from this project, and involve undergraduate students in research. This project has two thrusts. The first thrust aims to develop new marginal-based DPDS algorithms that improve upon the state-of-art in empirical evaluations. The tasks include: perform an in-depth study of the “marginal-to-dataset” problem (how to synthesize a dataset when given a set of marginals); develop and evaluate new approaches for handling numerical attributes; and develop adaptive and automated techniques for selecting marginals so that dataset synthesized with them captures as much useful information from the input dataset as possible. The second thrust complements the empirical research in the first thrust, and aims to develop statistical theory for high dimensional marginal-based data synthesis algorithms, and also a general learning theory framework to evaluate the utility of synthetic data in downstream tasks. The two thrusts are highly complementary and support each other. The experimental study in Thrust 1 will provide insights and directions for theoretical studies in Thrust 2, which will help explain the experimental findings as well as guide additional experimental studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

组织和机构收集的数据是当今信息时代的关键资源，并助长了当今经济的重要组成部分。但是，这些数据的披露对个人隐私构成了严重威胁。在保护隐私的同时，使用数据的一种重要方法是私人数据综合（DPD）。也就是说，作为输入一个私有数据集，人们使用不同的私有算法来生成与输入数据集“相似”的合成数据集。尽管DPD近年来引起了很多关注，但我们对该主题的理解仍然有限。该项目采用多学科的方法来提高我们的科学理解以及改进的DPD实践技术。更具体地说，该项目的新颖性如下。首先，它系统地探讨了基于边缘的DPD算法中的设计空间，这些算法已被证明在DPD的NIST竞争中有效，同时还从类似领域开发的数据合成技术（通常不满意DP）中获得了见解。其次，它发展了统计理论，即两者都是由DPDS算法的经验性能激发的，并指导这些算法的经验研究。该项目的重要性和重要性如下。我们在信息经济中。正在收集各种数据，例如在线互动，医疗传感器数据，基因组数据和位置数据。完全需要在保护个人隐私的同时使用这些数据的实用技术，并且将大大提高此类数据的价值。用户将从对其私人信息的控制中获得收益，整个社会将受益于从汇总数据中获得最大收益。 PIS计划基于该领域的现有研究以及该项目的研究共同开发和教授有关合成数据的研究生级课程，并让本科生参与研究。该项目有两个推力。第一个推力旨在开发新的基于边缘的DPD算法，以改善经验评估中的最先进。任务包括：对“边缘到数据”问题进行深入研究（在给出一组边缘时如何合成数据集）；开发和评估处理数值属性的新方法；并开发自适应和自动化技术来选择边缘，以便与它们合成的数据集从输入数据集中捕获尽可能多的有用信息。第二个推力在第一个推力中完成了实证研究，旨在为高维边缘数据综合算法开发统计理论，也是一个一般学习理论框架，以评估下游任务中合成数据的实用性。这两个推力是高度互补的，并互相支持。推力1中的实验研究将为推力2中的理论研究提供洞察力和方向，这将有助于解释实验发现，并指导其他实验研究。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估的珍贵的支​​持。

项目成果

FairRR: Pre-Processing for Group Fairness through Randomized Response

FairRR：通过随机响应进行群体公平性预处理

• 发表时间: 2024
• 期刊: Artificial Intelligence and Statistics
• 作者: Zeng, Xianli;Ward, Joshua;Cheng, Guang
• 通讯作者: Cheng, Guang

AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing

• DOI: 10.48550/arxiv.2310.15479
• 发表时间: 2023-10
• 期刊: ArXiv
• 作者: Namjoon Suh;Xiaofeng Lin;Din-Yin Hsieh;Merhdad Honarkhah;Guang Cheng

Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process

• 发表时间: 2022-10
• 作者: Yidong Ouyang;Liyan Xie;Guang Cheng

Sparse confidence sets for normal mean models

正态平均模型的稀疏置信集

• DOI: 10.1093/imaiai/iaad003
• 发表时间: 2023
• 期刊: Information and Inference: A Journal of the IMA
• 作者: Ning, Yang;Cheng, Guang
• 通讯作者: Cheng, Guang

Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions

• 发表时间: 2022-02
• 期刊: Trans. Mach. Learn. Res.
• 作者: Zhiying Fang;Guang Cheng