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TWC: Large: Collaborative: Computing Over Distributed Sensitive Data

TWC：大型：协作：分布式敏感数据计算

基本信息

批准号：
1565365
负责人：
Marco Gaboardi
金额：
$ 52.79万
依托单位：
SUNY at Buffalo
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-05-01 至 2020-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565365&HistoricalAwards=false
关键词：
TWC Large Collaborative Computing Over

项目摘要

Information about individuals is collected by a variety of organizations including government agencies, banks, hospitals, research institutions, and private companies. In many cases, sharing this data among organizations can bring benefits in social, scientific, business, and security domains, as the collected information is of similar nature, of about similar populations. However, much of this collected data is sensitive as it contains personal information, or information that could damage an organization's reputation or competitiveness. Sharing of data is hence often curbed for ethical, legal, or business reasons. This project develops a collection of tools that will enable the benefits of data sharing without having the data owners share the data. The techniques developed respect principles of data ownership and privacy requirement, and draw on recent scientific developments in privacy, cryptography, machine learning, computational statistics, program verification, and system security. The tools developed in this project will contribute to the existing research and business infrastructure, and hence enable new ways to create value in information whose use would have been otherwise restricted. The project supports the development of new curricula material and train a new generation of researchers and citizens with the multidisciplinary perspectives required to address the complex issues surrounding data privacy.

有关个人的信息由各种组织收集，包括政府机构、银行、医院、研究机构和私营公司。在许多情况下，在组织之间共享这些数据可以在社会，科学，商业和安全领域带来好处，因为收集的信息具有相似的性质，涉及相似的人群。然而，这些收集的数据中有许多是敏感的，因为它们包含个人信息，或者可能损害组织声誉或竞争力的信息。因此，出于道德、法律的或商业原因，数据共享常常受到限制。该项目开发了一系列工具，这些工具将在数据所有者不共享数据的情况下实现数据共享的好处。开发的技术尊重数据所有权和隐私要求的原则，并借鉴了隐私，密码学，机器学习，计算统计，程序验证和系统安全方面的最新科学发展。在这个项目中开发的工具将有助于现有的研究和商业基础设施，从而使新的方式来创造价值的信息，否则使用会受到限制。该项目支持开发新的课程材料，并培养新一代具有多学科视角的研究人员和公民，以解决围绕数据隐私的复杂问题。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation

DOI：
发表时间：
2021-06
期刊：
ArXiv
影响因子：
0
作者：
Gavin Brown;Marco Gaboardi;Adam D. Smith;Jonathan Ullman;Lydia Zakynthinou
通讯作者：
Gavin Brown;Marco Gaboardi;Adam D. Smith;Jonathan Ullman;Lydia Zakynthinou

Facility Location Problem in Differential Privacy Model Revisited