CAREER: Foundations of Privacy-Preserving Collaborative Learning

协作机器学习技术允许多个数据所有者通过增加数据的数量和多样性来合作训练更好的机器学习模型。然而，在许多现实场景中，数据是隐私敏感的，例如医疗记录、金融交易或地理位置数据。隐私保护机器学习技术可以促进机器学习应用程序，同时保护敏感数据的隐私。该项目旨在开发一种高效，安全和值得信赖的协作学习模式，以解决隐私保护协作学习在现实世界中的应用中面临的几个关键挑战。该项目的成果将允许多个数据所有者合作训练机器学习模型，而不会泄露任何敏感数据，这将通过增加数据的数量和多样性来提高机器学习应用程序的性能。它还将促进数据稀缺领域的新应用，并且由于隐私挑战，传统上合作受到限制，例如医疗保健中更好的药物和疫苗发现。该研究将通过指导本科生，开发新的本科生和研究生课程，以及为K-12学生和教师举办机器学习研讨会，与教育紧密结合，目标是建立多元化和包容性的机器学习劳动力。隐私保护机器学习有望通过允许大规模机器学习应用程序而不泄露任何敏感数据来彻底改变数据驱动的协作应用程序的未来，但其实际应用受到几个主要障碍的限制，包括通信瓶颈，安全性和可信度。这项研究将通过引入一种植根于信息和编码理论的新方法来解决这些基本挑战。该研究分为三个主要方面：1）开发通信高效的隐私保护协作学习的基础; 2）实现具有可证明的安全性和公平性保证的隐私保护机器学习范式; 3）在任意网络拓扑中实现隐私保护机器学习，包括集中式，分散式和动态拓扑，以及具有异构计算和通信资源的网络。这项研究植根于编码和信息理论，并结合了随机优化，分布式计算和密码学。从研究中获得的见解将使隐私感知机器学习应用程序能够：1）带宽和计算限制的用户可以访问，例如移动的边缘网络中的消费者设备; 2）安全，通过防止对手将不必要的行为注入决策过程;（3）对社会上的所有群体作出公正的决定，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

Collaborative machine-learning techniques allow multiple data owners to collaborate to train better machine-learning models by increasing the volume and diversity of data. In many real-world scenarios, however, the data is privacy-sensitive, as is the case for healthcare records, financial transactions, or geolocation data. Privacy-preserving machine-learning techniques can facilitate machine-learning applications while protecting the privacy of sensitive data. This project aims to develop an efficient, secure, and trustworthy collaborative learning paradigm to address several critical challenges in the real-world application of privacy-preserving collaborative learning. The outcomes of the project will allow multiple data owners to collaborate to train machine-learning models without revealing any sensitive data, which will improve the performance of machine-learning applications by increasing the volume and diversity of data. It will also facilitate novel applications in fields where data is scarce and collaboration has traditionally been limited due to privacy challenges, such as better drug and vaccine discovery in healthcare. The research will be strongly integrated with education, through mentoring of undergraduate students, development of new undergraduate and graduate courses, and machine-learning workshops for K-12 students and teachers, with the goal of building a diverse and inclusive machine learning workforce. Privacy-preserving machine learning is expected to revolutionize the future of data-driven collaborative applications, by allowing large-scale machine-learning applications without revealing any sensitive data, but its real-world adoption has been limited by several major barriers, including the communication bottleneck, security, and trustworthiness. The research will address these fundamental challenges by introducing a new approach rooted in information and coding theory. The research is organized in three main thrusts: 1) develop the foundations of communication-efficient privacy-preserving collaborative learning; 2) realize a privacy-preserving machine-learning paradigm with provable security and fairness guarantees; and 3) enable privacy-preserving machine learning in arbitrary network topologies, including centralized, decentralized, and dynamic topologies, and networks with heterogeneous computing and communication resources. The research is rooted in coding and information theory, and incorporates stochastic optimization, distributed computing, and cryptography. The insights gained from the research will enable privacy-aware machine learning applications that are: 1) accessible by users with bandwidth and computational limitations, such as consumer devices in mobile edge networks; 2) secure, by preventing adversaries from injecting unwanted behavior into the decision process; and 3) fair in its decisions towards all communities in society, without revealing any sensitive data and personal information.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.