TWC SBES: Medium: Utility for Private Data Sharing in Social Science

TWC SBES：媒介：社会科学中私人数据共享的实用程序

• 批准号: 1228669
• 负责人: Daniel Kifer
• 金额: $ 106.69万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228669&HistoricalAwards=false
• 关键词: TWC; SBES; Medium; Utility; Private

One of the keys to scientific progress is the sharing of research data. When the data contain information about human subjects, the incentives not to share data are stronger. The biggest concern is privacy - specific information about individuals must be protected at all times. Recent advances in mathematical notions of privacy have raised the hope that the data can be properly sanitized and distributed to other research groups without revealing information about any individual. In order to make this effort worthwhile, the sanitized data must be useful for statistical analysis. This project addresses the research challenges in making the sanitized data useful. The first part of the project deals with the design of algorithms that produce useful sanitized data subject to privacy constraints. The second part of the project deals with the development of tools for the statistical analysis of sanitized data. Existing statistical routines are not designed for the types of complex noise patterns that are found in sanitized data; their naive use will often result in missed discoveries or false claims of statistical significance. The target application for this project is a social science dataset with geographic characteristics. The intellectual merit of this proposal is the development of a utility theory for algorithms that sanitize data and statistical tools for their analysis. The broader impact is the improved ability of research groups to share useful, but privacy-preserving, research data.

科学进步的关键之一是研究数据的共享。当数据包含有关人类受试者的信息时，不共享数据的动机更强。最大的担忧是隐私--关于个人的特定信息必须在任何时候都受到保护。隐私的数学概念的最新进展带来了希望，即数据可以被适当地净化并分发给其他研究小组，而不会泄露任何个人的信息。为了使这项工作值得，净化后的数据必须对统计分析有用。该项目解决了使净化数据有用的研究挑战。该项目的第一部分涉及算法的设计，这些算法可以产生受隐私约束的有用的净化数据。该项目的第二部分涉及开发用于对净化数据进行统计分析的工具。现有的统计程序并不是为在净化数据中发现的复杂噪声模式类型而设计的;它们的天真使用通常会导致错过发现或错误的统计显著性声明。该项目的目标应用是具有地理特征的社会科学数据集。这一建议的智力价值是为净化数据和统计工具的算法开发了一个实用理论。更广泛的影响是，研究小组分享有用但保护隐私的研究数据的能力有所提高。

项目成果

Differentially Private Confidence Intervals for Empirical Risk Minimization

经验风险最小化的差分私人置信区间

• DOI: 10.29012/jpc.660
• 发表时间: 2019
• 期刊: Journal of Privacy and Confidentiality
• 作者: Wang, Yue;Kifer, Daniel;Lee, Jaewoo
• 通讯作者: Lee, Jaewoo

Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget

• DOI: 10.1145/3219819.3220076
• 发表时间: 2018-07
• 期刊: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Jaewoo Lee;Daniel Kifer

Statistical Approximating Distributions Under Differential Privacy

差分隐私下的统计近似分布

• DOI: 10.29012/jpc.666
• 发表时间: 2018
• 期刊: Journal of Privacy and Confidentiality
• 作者: Wang, Yue;Kifer, Daniel;Lee, Jaewoo;Karwa, Vishesh
• 通讯作者: Karwa, Vishesh

A Simple Baseline for Travel Time Estimation using Large-scale Trip Data

• DOI: 10.1145/3293317
• 发表时间: 2019-02-01
• 期刊: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
• 影响因子: 5
• 作者: Wang, Hongjian;Tang, Xianfeng;Li, Zhenhui
• 通讯作者: Li, Zhenhui