CAREER: Foundations of Privacy-Preserving Collaborative Learning

职业：隐私保护协作学习的基础

• 批准号: 2144927
• 负责人: Basak Guler
• 金额: $ 54.1万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144927&HistoricalAwards=false
• 关键词: CAREER; Foundations; Privacy; Preserving; Collaborative

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.

协作机器学习技术使多个数据所有者可以通过增加数据的数量和多样性来协作以训练更好的机器学习模型。但是，在许多实际情况下，数据对医疗记录，金融交易或地理分配数据的情况也对隐私敏感。隐私的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有效，安全和值得信赖的协作学习范式，以应对现实保护隐私合作学习的实际挑战。该项目的结果将允许多个数据所有者在不透露任何敏感数据的情况下协作培训机器学习模型，这将通过增加数据的量和多样性来改善机器学习应用程序的性能。它还将促进数据稀缺的领域中的新应用，并且由于隐私挑战而传统上限制了协作，例如医疗保健中的药物和疫苗发现更好。这项研究将通过教育，通过指导本科生，新的本科和研究生课程的发展以及针对K-12学生和老师的机器学习讲习班的强烈融合，目的是建立一个多样化且包容性的机器学习员工。预计隐私的机器学习有望通过允许大规模的机器学习应用程序而不会透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，但是其现实世界中的采用受到了几个主要障碍的限制，包括通信瓶颈，安全性，安全性和可信赖性。该研究将通过引入植根于信息和编码理论的新方法来应对这些基本挑战。这项研究是在三个主要推力中组织的：1）开发沟通效率保护隐私的协作学习的基础； 2）实现具有可证明的安全性和公平保证的保护隐私的机器学习范式； 3）在任意网络拓扑中启用隐私的机器学习，包括集中式，分散和动态拓扑以及具有异质计算和通信资源的网络。该研究植根于编码和信息理论，并结合了随机优化，分布式计算和加密术。从研究中获得的见解将启用：1）具有带宽和计算限制的用户，例如移动边缘网络中的消费者设备； 2）安全，防止对手向决策过程注入不必要的行为； 3）在对社会上所有社区的决定方面的公平，没有揭示任何敏感的数据和个人信息。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优点和更广泛的影响评估标准通过评估来支持的。