SaTC: CORE: Small: New Approaches to Decentralized Differential Privacy

SaTC：核心：小型：去中心化差异隐私的新方法

• 批准号: 1816028
• 负责人: Jonathan Ullman
• 金额: $ 50万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816028&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; New; Approaches

Recently, Google and Apple have deployed large systems for differentially private collection and analysis of decentralized user data. These systems use a local model of privacy in which no sensitive user data is collected. This local model enjoys many implementation advantages, but does not capture the most expressive private algorithms. These more expressive private algorithms inherently require a central model of privacy, in which a trusted party agrees to collect the sensitive data and reveal only the outcome of some private algorithm. Finding a trusted aggregator can be problematic in many applications. This project specifically addresses this tension by using cryptography to design and implement scalable secure protocols for the statistical analysis of decentralized user data that combine the best features of the central and local models of privacy.This project introduces and studies a novel intermediate model for decentralized privacy based on anonymous computation. Protocols in this model enjoy the same simplicity as protocols in the local model of privacy, but bypass some of the limitations of protocols in the local model. For functionalities that cannot be achieved in this intermediate model, this project designs tailored secure cryptographic to implement these functionalities without a trusted data collector, while overcoming the high communication, computation, and round complexity overheads of generic protocols. The investigators will involve graduate and undergraduate students in this research.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.

最近，谷歌和苹果已经部署了大型系统，用于分散用户数据的差异化私人收集和分析。 这些系统使用本地隐私模型，其中不收集敏感的用户数据。这种局部模型具有许多实现上的优点，但不能捕获最具表现力的私有算法。 这些更具表现力的私有算法本质上需要一个中央隐私模型，其中受信任方同意收集敏感数据并仅透露某些私有算法的结果。在许多应用程序中，找到一个可信的聚合器可能是一个问题。本项目针对这一矛盾，利用密码学设计和实现了一种可扩展的安全协议，用于对分散用户数据进行统计分析，该协议联合收割机了中心隐私模型和局部隐私模型的最佳特性，并引入和研究了一种基于匿名计算的新型分散隐私中间模型。 该模型中的协议与本地隐私模型中的协议一样简单，但绕过了本地模型中协议的一些限制。 对于在这个中间模型中无法实现的功能，该项目设计了量身定制的安全密码来实现这些功能，而无需可信的数据收集器，同时克服了通用协议的高通信，计算和轮复杂性开销。研究人员将让研究生和本科生参与这项研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Improved Straight-Line Extraction in the Random Oracle Model With Applications to Signature Aggregation

• DOI: 10.1007/978-3-031-22966-4_10
• 发表时间: 2022
• 期刊: IACR Cryptol. ePrint Arch.
• 作者: Yashvanth Kondi;Abhi Shelat

The limits of pan privacy and shuffle privacy for learning and estimation

• DOI: 10.1145/3406325.3450995
• 发表时间: 2020-09
• 期刊: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
• 作者: Albert Cheu;Jonathan Ullman

A Private and Computationally-Efficient Estimator for Unbounded Gaussians

• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: Gautam Kamath;Argyris Mouzakis;Vikrant Singhal;T. Steinke;Jonathan Ullman

Guaranteed Output in ?(sqrt{?}) Rounds for Round-Robin Sampling Protocols

循环采样协议的 ?(sqrt{?}) 轮保证输出

• 发表时间: 2022
• 期刊: EUROCRYPT'22
• 作者: Cohen, Ran;Doerner, Jack;Kondi, Yashvanth;Shelat, abhi.
• 通讯作者: Shelat, abhi.

Securely Sampling Biased Coins with Applications to Differential Privacy

• DOI: 10.1145/3319535.3354256
• 发表时间: 2019-11
• 期刊: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
• 作者: J. Champion;Abhi Shelat;Jonathan Ullman