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SaTC: CORE: Small: Multi-Party High-dimensional Machine Learning with Privacy

SaTC：核心：小型：具有隐私性的多方高维机器学习

基本信息

批准号：
1717950
负责人：
David Evans
金额：
$ 49.86万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717950&HistoricalAwards=false
关键词：
SaTC CORE Small Multi Party

项目摘要

Individuals and organizations can frequently benefit from combining their data to learn collective models. However, combining data to enable multi-party learning is often not possible. It may not be permitted due to privacy policies, or may be considered too risky for a business to expose its own data to others. In addition, high-dimensional data are prevalent in modern data-driven applications. Learning from high-dimensional data owned by differential organizations is even more challenging, due to the bias introduced by the high-dimensional machine learning methods. The overarching goal of this project is to address these challenges by developing methods that enable a group of mutually distrusting parties to securely collaborate to apply high dimensional machine learning methods to produce a joint model without exposing their own data. This project enables owners of sensitive data to jointly learn models across their datasets without exposing that data and providing meaningful privacy guarantees. It produces open source software tools and has many important societal applications, including its use in analyzing electronic health records across multiple hospitals to identify medical correlations what could not be found by any individual hospital. The key of multi-party high-dimensional machine learning is to find an efficient way to produce an accurate aggregate model that reflects all of the data, by combining local models that are developed independently based on individual data sets. The strategy of this project is to combine two emerging research directions: distributed machine learning, which seeks to distribute machine learning algorithms across hosts and produce an aggregate model by combining multiple local models; and secure multi-party computation, which enables a group of mutually distrusting parties to jointly compute a function without leaking information about their private inputs or any intermediate results. It also incorporates differential privacy-based mechanisms into multi-party high dimensional learning, which further protects the individual data points in each party. The results of this research have the potential to impact both the machine learning and security research communities. The education plan of this project includes developing open course materials that integrate privacy and machine learning, and provide research-based training opportunities for both undergraduate and graduate students in computer science, systems engineering, and medical informatics. It actively gets underrepresented groups involved in research projects, and trains a new generation of interdisciplinary researchers.

个人和组织经常可以从合并他们的数据来学习集体模型中受益。然而，组合数据以实现多方学习通常是不可能的。由于隐私政策，这可能是不允许的，或者可能被认为风险太大，企业不能将自己的数据暴露给其他人。此外，高维数据在现代数据驱动的应用程序中非常普遍。由于高维机器学习方法引入的偏差，从不同组织拥有的高维数据中学习甚至更具挑战性。该项目的总体目标是通过开发方法来应对这些挑战，这些方法使一群相互不信任的各方能够安全地合作，应用高维机器学习方法来产生联合模型，而不会暴露他们自己的数据。该项目使敏感数据的所有者能够共同学习其数据集中的模型，而无需公开该数据并提供有意义的隐私保证。它生产开源软件工具，并拥有许多重要的社会应用程序，包括用于分析多家医院的电子健康记录，以确定任何一家医院都找不到的医疗关联。多方高维机器学习的关键是通过结合基于单个数据集独立开发的局部模型，找到一种有效的方法来产生反映所有数据的准确聚合模型。该项目的策略是结合两个新兴的研究方向：分布式机器学习，寻求跨主机分发机器学习算法，并通过组合多个本地模型生成聚合模型；以及安全多方计算，使一组相互不信任的各方能够联合计算函数，而不会泄露关于他们的私人输入或任何中间结果的信息。它还将基于隐私的差异机制融入多方高维学习中，进一步保护了各方的个人数据点。这项研究的结果有可能对机器学习和安全研究社区产生影响。该项目的教育计划包括开发融合隐私和机器学习的开放课程材料，并为本科生和研究生提供计算机科学、系统工程和医学信息学的研究型培训机会。它积极地让代表不足的群体参与研究项目，并培训新一代跨学科的研究人员。