CAREER: Differentially-Private Machine Learning with Applications to Biomedical Informatics

职业：差分隐私机器学习及其在生物医学信息学中的应用

• 批准号: 1253942
• 负责人: Kamalika Chaudhuri
• 金额: $ 49.06万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1253942&HistoricalAwards=false
• 关键词: CAREER; Differentially; Private; Machine; Learning

Machine learning on large-scale patient medical records can lead to the discovery of novel population-wide patterns enabling advances in genetics, disease mechanisms, drug discovery, healthcare policy, and public health. However, concerns over patient privacy prevent biomedical researchers from running their algorithms on large volumes of patient data, creating a barrier to important new discoveries through machine-learning. The goal of this project is to address this barrier by developing privacy-preserving tools to query, cluster, classify and analyze medical databases. In particular, the project aims to ensure differential privacy --- a formal mathematical notion of privacy designed by cryptographers which has gained considerable attention in the systems, algorithms, machine-learning and data-mining communities in recent years. The primary challenge in applying differentially-private machine learning tools to biomedical informatics is the lack of statistical efficiency, or the large number of samples required.The project will overcome this challenge by drawing on insights obtained from the PI's expertise to develop differentially-private and highly statistically-efficient machine learning tools for classification and clustering. The proposed research will advance the state-of-the-art in privacy-preserving data analysis by combining insights from differential privacy, statistics, machine learning, and database algorithms. The proposed research is closely tied to the development of the undergraduate and graduate curricula at UCSD, feeding into the PI's new undergraduate machine learning class, a new graduate learning theory class, and updates to an algorithm design and analysis class. The corresponding materials will be publicly disseminated through the PI's website. The PI is strongly committed to increasing the participation of women and minorities, and will engage in outreach activities to attract and retain women in computer science.

对大规模患者病历的机器学习可以发现新的人群范围模式，从而促进遗传学、疾病机制、药物发现、医疗保健政策和公共卫生的进步。然而，对患者隐私的担忧阻止生物医学研究人员在大量患者数据上运行他们的算法，从而为通过机器学习获得重要的新发现设置了障碍。该项目的目标是通过开发隐私保护工具来查询、聚类、分类和分析医疗数据库来解决这一障碍。特别是，该项目旨在确保差异隐私——密码学家设计的一种正式的隐私数学概念，近年来在系统、算法、机器学习和数据挖掘社区中获得了相当多的关注。 将差分隐私机器学习工具应用于生物医学信息学的主要挑战是缺乏统计效率，或者需要大量样本。该项目将通过利用从 PI 的专业知识中获得的见解来开发差分隐私和用于分类和聚类的统计效率极高的机器学习工具。拟议的研究将通过结合差异隐私、统计学、机器学习和数据库算法的见解，推进隐私保护数据分析的最先进水平。拟议的研究与加州大学圣地亚哥分校本科生和研究生课程的发展密切相关，为 PI 的新本科机器学习课程、新的研究生学习理论课程以及算法设计和分析课程的更新提供支持。 相应材料将通过PI网站公开发布。 PI 坚定致力于增加女性和少数族裔的参与，并将开展外展活动以吸引和留住计算机科学领域的女性。