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收集为研究人员，公司和决策者提供了重大好处。为了保护个人隐私，许多组织免受公共部门和私营部门的侵害，都采用了不同的隐私作为严格的隐私措施。但是，差异隐私的最新部署揭示了关键的研究差距。首先，差异隐私中的许多现有理论工作都集中在最坏的分析上，这通常会导致过度悲观的结果，并且在实践中未能为算法设计提供信息。尽管最近进步，但用于机器学习和数据共享的私人算法仍未被广泛采用。最后，缺乏用于隐私风险评估的全面工具使从业者很难评估差异隐私的有效性并确定适当的隐私风险参数。 该项目旨在通过扩大保护隐私算法的曲目并开发审计机制来评估这些算法提供的隐私保护，以应对差异隐私的这些挑战。研究侧重于两个基本和紧密相关的问题：私人学习和私人综合数据。在私人学习中，目标是使用具有不同隐私保证的敏感数据来学习准确的机器学习模型。在私人合成数据中，目标是私密地生成一个合成数据集，该数据集保留了敏感数据集的重要统计趋势。该项目通过三个研究推力来推动这两个问题的前沿。第一个推力发展了一个理论框架，该框架超出了悲观的最坏情况分析，可以更好地捕获实际场景和指导算法设计。第二个推力设计实践算法是由实践中问题的理论原理和经验结构所告知的。第三个重点是评估学习和合成数据算法的隐私风险的隐私攻击和审计机制。该项目还包括一项全面的教育和外展计划，为不同教育水平的学生提供研究机会，并开发新的课程和教育材料。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的评估和更广泛的影响来通过评估来支持的。