CAREER: Theory and Practice of Privacy-Utility Tradeoffs in Enterprise Data Sharing

职业：企业数据共享中隐私与效用权衡的理论与实践

• 批准号: 2338772
• 负责人: Giulia Fanti
• 金额: $ 59.73万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338772&HistoricalAwards=false
• 关键词: CAREER; Theory; Practice; Privacy; Utility; CAREER; Theory; Practice; Privacy; Utility

A cooperative and coordinated effort across organizations will be required for effectively addressing many of today's biggest global challenges and societal problems, including cybercrime, climate change, and public health. Data sharing will enable organizations involving academic researchers, governments, and industry to collaborate more effectively on solving such problems. However, in practice data sharing among organizations is hindered by two significant factors. (1) Organizations often fear inadvertently leaking trade-secrets, such as business strategies, and therefore do not share data; this concern is called trade-secret privacy. (2) When organizations have access to shared data, they often lack the in-house resources to evaluate the quality and usefulness of the data-source; this problem is called data source utility. This project quantitatively addresses the data sharing problems in terms of these two factors. Specifically, the project will develop quantitative methods for measuring trade-secret privacy and data-source utility, assessing the tradeoffs between these two factors, and developing algorithms that come close to optimizing this tradeoff. This research will help encourage greater data sharing among organizations, thereby enhancing society's ability to address global challenges through informed collaboration. Several outreach and education activities complement and integrate the research. These include working with companies to develop privacy protections in their applications and services, working on an open source library for trade-secret privacy and utility, and organizing research internship programs for students in Africa. This project aims to design novel privacy and utility metrics and frameworks to help organizations make more informed choices regarding data sharing. Both of the above problems (trade secret privacy and data source utility) can be framed as a study of divergences between probability distributions. Building on the investigator's prior work studying divergences in the context of deep generative models, this project will study how to carefully select appropriate divergence measures to (a) satisfy enterprise use cases, and (b) provide strong theoretical guarantees of privacy and utility. The project will proceed in four thrusts. Thrust 1 will define and analyze a metric for trade secret privacy. This metric will be based on the notion of maximal leakage from information theory; maximal leakage captures the maximum amount of information that can be gained by an adversary about any secret quantity after seeing released, obfuscated data. The proposed metric in this project will differ by modeling information leakage of specific trade secrets, rather than any arbitrary secret. Thrust 2 will propose and theoretically analyze a metric for data source utility, based on statistical divergences over probability distributions. This work will build on the expansive literature on data valuation. Thrust 3 will study fundamental tradeoffs between these metrics; the goal will be to identify algorithms that approach the fundamental bounds. Thrust 4 will analyze downstream performance guarantees, which connect the proposed privacy and utility metrics to enterprise use cases motivated by the investigator's ongoing industry collaborations. In summary, the project will contribute a formal methodology for modeling and mitigating common data sharing problems in enterprise settings.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.

有效解决当今许多最大的全球挑战和社会问题，包括网络犯罪，气候变化和公共卫生，需要进行整个组织的合作和协调的努力。数据共享将使涉及学术研究人员，政府和行业的组织能够更有效地解决此类问题。但是，实际上，组织之间的数据共享受到了两个重要因素的阻碍。 （1）组织经常担心会无意中泄漏贸易销售，例如业务策略，因此不共享数据；这种担忧称为贸易保密。 （2）当组织可以访问共享数据时，他们通常缺乏内部资源来评估数据源的质量和实用性；此问题称为数据源实用程序。该项目定量解决了这两个因素的数据共享问题。具体而言，该项目将开发定量方法来衡量贸易保密和数据源实用程序，评估这两个因素之间的权衡，并开发接近优化此权衡的算法。这项研究将有助于鼓励组织之间的更多数据共享，从而增强了社会通过知情合作解决全球挑战的能力。 几项宣传和教育活动补充并整合了研究。其中包括与公司合作开发其应用程序和服务中的隐私保护，在开源库上为贸易秘密隐私和公用事业公司工作，并为非洲学生组织研究实习计划。 该项目旨在设计新颖的隐私和公用事业指标和框架，以帮助组织在数据共享方面做出更明智的选择。以上两个问题（商业秘密隐私和数据源实用程序）都可以作为对概率分布之间差异的研究进行构架。在研究者先前研究差异的工作基础上，该项目将研究如何仔细选择适当的分歧措施以（a）满足企业用例，并且（b）提供了强大的隐私和效用理论保证。该项目将分四个推力进行。推力1将定义和分析商业秘密隐私的指标。该指标将基于信息理论中最大泄漏的概念；最大泄漏可捕获对手可以在看到释放，混淆的数据后获得任何秘密数量的最大信息。该项目中提出的指标将通过对特定商业秘密的信息泄漏而不是任何任意秘密进行建模。推力2将基于统计差异对概率分布的统计差异提出并理论分析数据源实用程序的度量。这项工作将基于有关数据评估的广泛文献。推力3将研究这些指标之间的基本权衡；目标是确定接近基本界限的算法。 Thrust 4将分析下游绩效保证，该保证将拟议的隐私和公用事业指标与研究人员正在进行的行业合作激励的企业用例联系起来。总而言之，该项目将为企业设置中的常见数据共享问题建模和缓解常见数据共享问题做出形式化。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。