CAREER: New Frontiers of Private Learning and Synthetic Data

职业：私人学习和合成数据的新领域

• 批准号: 2339775
• 负责人: Zhiwei Steven Wu
• 金额: $ 68万
• 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=2339775&HistoricalAwards=false
• 关键词: CAREER; New; Frontiers; Private; Learning

The vast collection of detailed personal data offers significant benefits to researchers, companies, and policymakers. To protect individual privacy, many organizations, both from the public and private sectors, have adopted differential privacy as a rigorous privacy measure. However, recent deployments of differential privacy have revealed key research gaps. First, much of the existing theoretical work in differential privacy focuses on worst-case analyses, which often lead to overly pessimistic results and fail to inform algorithm design in practice. Despite recent advancements, differentially private algorithms for machine learning and data sharing are still not widely adopted technologies. Lastly, the lack of comprehensive tools for privacy risk assessment makes it difficult for practitioners to evaluate the effectiveness of differential privacy and to determine appropriate privacy risk parameters. This project aims to address these challenges in differential privacy by expanding the repertoire of privacy-preserving algorithms and developing auditing mechanisms to assess the privacy protection these algorithms provide.The research focuses on two fundamental and closely related problems: private learning and private synthetic data. In private learning, the goal is to learn accurate machine learning models using sensitive data with differential privacy guarantees. In private synthetic data, the goal is to differentially privately generate a synthetic dataset that preserves important statistical trends of the sensitive dataset. The project advances the frontiers of these two problems with three research thrusts. The first thrust develops a theoretical framework that goes beyond pessimistic worst-case analyses to better capture practical scenarios and guide algorithm design. The second thrust designs practical algorithms that are informed by theoretical principles and empirical structures of the problems in practice. The third focuses on privacy attacks and auditing mechanisms that evaluate the privacy risks of learning and synthetic data algorithms. The project also includes a comprehensive educational and outreach program, providing research opportunities for students at different educational levels and developing new courses and educational materials.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.

大量详细的个人数据为研究人员、公司和政策制定者带来了巨大的好处。为了保护个人隐私，许多公共和私营部门的组织都采用差异隐私作为严格的隐私措施。然而，最近差分隐私的部署揭示了关键的研究差距。首先，现有的差分隐私理论工作大多集中于最坏情况分析，这往往会导致过于悲观的结果，并且无法为实践中的算法设计提供信息。尽管最近取得了进展，但用于机器学习和数据共享的差分隐私算法仍然没有被广泛采用的技术。最后，缺乏全面的隐私风险评估工具，使得从业者难以评估差异隐私的有效性并确定合适的隐私风险参数。 该项目旨在通过扩展隐私保护算法的范围并开发审核机制来评估这些算法提供的隐私保护来解决差异隐私中的这些挑战。该研究重点关注两个基本且密切相关的问题：私人学习和私人合成数据。在私人学习中，目标是使用具有差异隐私保证的敏感数据来学习准确的机器学习模型。在私有合成数据中，目标是差异化地私有生成一个合成数据集，以保留敏感数据集的重要统计趋势。该项目通过三个研究重点推进了这两个问题的前沿。第一个推动力开发了一个超越悲观的最坏情况分析的理论框架，以更好地捕捉实际场景并指导算法设计。第二个主旨是根据实践中问题的理论原理和经验结构设计实用算法。第三个重点是隐私攻击和评估学习和合成数据算法的隐私风险的审计机制。该项目还包括全面的教育和推广计划，为不同教育水平的学生提供研究机会，并开发新课程和教材。该奖项反映了 NSF 的法定使命，通过使用基金会的智力优势和更广泛的评估，被认为值得支持。影响审查标准。