CIF: Small: Information-theoretic privacy and security for personalized distributed learning

CIF：小型：个性化分布式学习的信息论隐私和安全

基本信息

批准号：
2139304
负责人：
Suhas Diggavi
金额：
$ 50万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139304&HistoricalAwards=false
关键词：
CIF Small Information theoretic privacy

项目摘要

Personalized recommendation systems have been widely deployed with centralized data collection over the web and mobile applications with great success. Concurrently, they have created significant and justified public concerns about privacy. Society is in the midst of crossing a new frontier where millions of devices ranging from simpler Internet-of-Things devices (sensing) to (semi) autonomous vehicles (cars, drones) are connected over networks. A natural question is whether and how one can leverage large-scale local data collection in this emerging ecosystem to collaboratively build distributed personalized learning systems. This motivates the central question of this project, namely, how to design personalized learning models with information-theoretic privacy and security guarantees. The research outcomes of this project will be broadly disseminated, through publications, involvement of the PI in teaching and interaction with industry. One would ideally like to design personalized systems that can leverage large-scale collaboration, maintain privacy of local data, and require trust only on one's own devices as opposed to other entities. This project therefore explores how to design privacy schemes which also give good personalized learning performance, and how to design robust collaborative schemes that give good personalized learning performance despite malicious participants. In particular, in the task on privacy for personalized learning, the project plans to explore designs of personalized privacy mechanisms that are robust to iterative interactions necessary for collaborative learning and analyze its privacy-performance trade-off using information-theoretic and statistical tools. In the task on security for personalized learning, the project leverages ideas from high-dimensional robust statistics and information theory to develop robust mechanisms with theoretical guarantees to enable personalized learning in the presence of malicious participating devices, including investigating when collaboration is beneficial. The successful completion of the project will advance the state of the art and build bridges between information theory and trustworthy federated/distributed machine learning and optimization, through formal privacy and security guarantees without adversary computational assumptions.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.

个性化的推荐系统已通过网络和移动应用程序广泛部署，并取得了巨大的成功。同时，他们对隐私产生了重要的公众关注。社会正越过一个新的边界，其中数百万个设备从更简单的互联网设备（传感）到（半）自动驾驶汽车（汽车，无人机）在网络上连接。一个自然的问题是，人们是否以及如何利用本新兴生态系统中的大规模本地数据收集来协作建立分布式的个性化学习系统。这激发了该项目的核心问题，即如何使用信息理论隐私和安全保证设计个性化学习模型。该项目的研究成果将通过出版物的介入PI参与与行业的教学和互动。理想情况下，人们希望设计可以利用大规模协作，维护本地数据的隐私并仅在自己的设备上而不是其他实体的设备上的信任的个性化系统。因此，该项目探讨了如何设计隐私方案，这些计划也提供了良好的个性化学习表现，以及如何设计强大的协作方案，尽管有恶意的参与者，这些方案仍能为良好的个性化学习表现。特别是，在针对个性化学习的隐私任务中，该项目计划探索个性化的隐私机制的设计，这些设计对协作学习所需的迭代互动是可靠的，并使用信息理论和统计工具分析了其隐私性绩效折衷。在针对个性化学习的安全任务中，该项目利用高维鲁棒的统计数据和信息理论的想法来开发具有理论保证的强大机制，可以在存在恶意参与设备的情况下实现个性化学习，包括在协作时进行调查。该项目的成功完成将推进艺术的状态，并通过正式的隐私和安全保证在没有对手计算假设的情况下通过正式的隐私和安全保证在信息理论与可信赖的联合/分布式机器学习和优化之间建立桥梁。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是值得的。