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

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.

为了有效应对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，需要各组织之间的合作和协调努力。数据共享将使学术研究人员、政府和工业界能够更有效地合作解决这些问题。然而，在实践中，组织之间的数据共享受到两个重要因素的阻碍。(1)组织通常担心无意中泄露商业秘密，例如商业策略，因此不共享数据;这种担忧被称为商业秘密隐私。(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有用性;这个问题称为数据源实用性。该项目定量地解决了这两个因素方面的数据共享问题。具体来说，该项目将开发用于衡量商业秘密隐私和数据源效用的定量方法，评估这两个因素之间的权衡，并开发接近优化这一权衡的算法。这项研究将有助于鼓励各组织之间更多地分享数据，从而提高社会通过知情合作应对全球挑战的能力。 一些推广和教育活动补充和整合了研究。这些措施包括与公司合作，在其应用程序和服务中开发隐私保护，开发商业秘密隐私和实用程序的开源库，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实用性指标和框架，以帮助组织在数据共享方面做出更明智的选择。上述两个问题（商业秘密隐私和数据源效用）都可以被视为概率分布之间的差异研究。基于研究人员先前在深度生成模型背景下研究分歧的工作，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，（B）提供隐私和实用性的强有力的理论保证。该项目将分四个方面进行。重点1将定义和分析商业秘密隐私的指标。该度量将基于信息理论中的最大泄漏概念;最大泄漏捕获了对手在看到发布的混淆数据后可以获得的关于任何秘密数量的最大信息量。在这个项目中提出的度量将通过建模特定的商业秘密，而不是任何任意的秘密的信息泄露而有所不同。重点2将根据概率分布的统计差异，提出并从理论上分析数据源效用的度量。这项工作将建立在数据估值的广泛文献。目标3将研究这些指标之间的基本权衡;目标是确定接近基本边界的算法。Thrust 4将分析下游性能保证，将拟议的隐私和实用性指标与调查人员正在进行的行业合作所激发的企业用例联系起来。总之，该项目将为建模和缓解企业环境中常见的数据共享问题提供一种正式的方法。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。