SaTC: CORE: Small: New Techniques for Optimizing Accuracy in Differential Privacy Applications

SaTC：核心：小型：优化差异隐私应用准确性的新技术

• 批准号: 1931686
• 负责人: Daniel Kifer
• 金额: $ 50万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931686&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; New; Techniques

Differential Privacy is an important advance in the modern toolkit for protecting privacy and confidentiality. It allows organizations such as government agencies and private companies to collect data and publish statistics about it without leaking personal information about people -- no matter how sophisticated an attacker is. The project's novelties are in the careful design of new differentially private tools that provide more accurate population statistics while maintaining strong privacy guarantees. The project's impacts are in the ability to create datasets for social science and policy research without sacrificing privacy of individuals. The project includes both graduate and undergraduate students in this research. The technical ideas behind this project are that a careful analysis of privacy proofs for many differentially private algorithms can identify additional (noisy) information that can be released without changing the privacy guarantees. In addition to this, noise that is correlated between different stages of a differentially private algorithm can further reduce the variance of the final result. These techniques will allow smaller organizations to optimize the accuracy of their privacy preserving algorithms for practical deployment, and customize existing algorithms by swapping in building blocks that have more accuracy and that allow them to take better advantage of background knowledge.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.

差异隐私是保护隐私和机密性的现代工具包中的重要进步。它允许政府机构和私人公司等组织收集数据并发布有关该数据的统计信息，而无需泄漏有关人员的个人信息 - 无论攻击者多么复杂。该项目的新颖性在于仔细设计新的私人工具，这些工具提供了更准确的人口统计数据，同时保持强大的隐私保证。该项目的影响在于能够为社会科学和政策研究创建数据集而不牺牲个人隐私。该项目在本研究中包括研究生和本科生。 该项目背后的技术思想是，对许多差异私人算法的隐私证明进行仔细分析可以识别出可以在不更改隐私保证的情况下发布的其他（嘈杂）信息。除此之外，差异私有算法的不同阶段之间相关的噪声可以进一步降低最终结果的方差。这些技术将使较小的组织能够优化其隐私保存算法的实际部署算法的准确性，并通过交换具有更准确性的构件，并可以更好地利用背景知识来定制现有算法。该奖项反映了NSF的法定任务，并通过评估了基金会的范围来反映支持者的支持，并通过基金会的范围进行了评估和宽广的效果。

项目成果

Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping

• DOI: 10.2478/popets-2021-0008
• 发表时间: 2020-09
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Jaewoo Lee;Daniel Kifer

Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms

来自指数机制、稀疏向量、噪声最大值和相关算法的自由间隙估计

• DOI: 10.1007/s00778-022-00728-2
• 发表时间: 2022
• 期刊: The VLDB Journal
• 作者: Ding, Zeyu;Wang, Yuxin;Xiao, Yingtai;Wang, Guanhong;Zhang, Danfeng;Kifer, Daniel
• 通讯作者: Kifer, Daniel

Answering Private Linear Queries Adaptively Using the Common Mechanism

使用通用机制自适应地回答私有线性查询

• DOI: 10.14778/3594512.3594519
• 发表时间: 2023
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Xiao, Yingtai;Wang, Guanhong;Zhang, Danfeng;Kifer, Daniel
• 通讯作者: Kifer, Daniel

Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms

• DOI: 10.14778/3368289.3368295
• 发表时间: 2019-11-01
• 期刊: PROCEEDINGS OF THE VLDB ENDOWMENT
• 影响因子: 2.5
• 作者: Ding, Zeyu;Wang, Yuxin;Kifer, Daniel
• 通讯作者: Kifer, Daniel

Optimizing Fitness-For-Use of Differentially Private Linear Queries

• DOI: 10.14778/3467861.3467864
• 发表时间: 2020-11
• 期刊: ArXiv
• 作者: Yingtai Xiao;Zeyu Ding;Yuxin Wang;Danfeng Zhang;Daniel Kifer