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

基本信息

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

项目摘要

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，是当今经济的重要组成部分，然而，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差异化私人数据。也就是说，给定一个私有数据集作为输入，使用一种差分私有算法来生成与输入数据集“相似”的合成数据集，尽管 DPDS 近年来受到了广泛关注，但我们对这一主题的理解仍然存在。该项目仍然有限。更具体地说，该项目的新颖之处如下：首先，它系统地探索了已被证明有效的基于边际的 DPDS 算法的设计空间。 NIST 在 DPDS 上的竞赛，同时也从类似领域开发的数据合成技术中汲取见解（通常不满足 DP）其次，它发展了统计理论，这些理论都是由 DPDS 算法的经验性能驱动的，并指导这些算法的实证研究。。该项目的更广泛意义和重要性如下：我们正在收集各种数据，例如在线交互、医疗传感器数据、基因组数据和位置数据，同时可以使用这些数据。保护个人隐私是迫切需要的，这将极大地提高这些数据的价值，用户将从加强对其私人信息的控制中获益，整个社会将从 PI 共同开发和教授数据的计划中获得最大利益。研究生水平课程该项目有两个重点，旨在开发新的基于边际的 DPDS 算法，以改进状态。实证评估中的艺术任务包括：深入研究“边际到数据集”问题（在给定一组边际时如何合成数据集）；并开发自适应和自动化的选择技术第二个主旨补充了第一个主旨中的实证研究，旨在为高维基于边际的数据合成算法开发统计理论。评估合成数据在下游任务中的效用的通用学习理论框架这两个主旨是高度互补的，并且相互支持。主旨 1 中的实验研究将为主旨 2 中的理论研究提供见解和方向，这将有助于解释实验。调查结果以及附加指南该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。