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

个人和组织经常可以从组合他们的数据来学习集体模型中获益。然而，结合数据来实现多方学习通常是不可能的。由于隐私政策的原因，它可能不被允许，或者可能被认为对企业来说，将自己的数据暴露给其他人风险太大。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带来的偏见，从不同组织拥有的高维数据中学习更具挑战性。该项目的总体目标是通过开发方法来解决这些挑战，这些方法使一组相互不信任的各方能够安全地协作，应用高维机器学习方法来生成联合模型，而不会暴露自己的数据。该项目使敏感数据的所有者能够在不暴露数据并提供有意义的隐私保证的情况下，跨数据集共同学习模型。它生产开源软件工具，并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无法找到的医疗相关性。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，它寻求将机器学习算法分布在主机上，并通过组合多个本地模型产生聚合模型；安全的多方计算，使一组互不信任的各方能够共同计算一个函数，而不会泄露他们的私人输入信息或任何中间结果。它还将基于隐私的差异性机制融入到多方高维学习中，进一步保护了各方的个体数据点。这项研究的结果有可能影响机器学习和安全研究社区。该项目的教育计划包括开发整合隐私和机器学习的开放课程材料，并为计算机科学，系统工程和医学信息学的本科生和研究生提供基于研究的培训机会。它积极让代表性不足的群体参与研究项目，并培养新一代跨学科研究人员。

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.