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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.

个性化推荐系统已经被广泛部署，具有通过web和移动的应用的集中式数据收集，并取得了巨大的成功。与此同时，它们引起了公众对隐私的重大且合理的担忧。社会正在跨越一个新的边界，从简单的物联网设备（传感）到（半）自动驾驶汽车（汽车，无人机）的数百万设备通过网络连接。一个自然的问题是，人们是否以及如何利用这个新兴生态系统中的大规模本地数据收集来协作构建分布式个性化学习系统。这激发了本项目的中心问题，即，如何设计个性化的学习模型与信息理论的隐私和安全保证。该项目的研究成果将通过出版物、PI参与教学和与行业互动等方式广泛传播。理想情况下，人们希望设计能够利用大规模协作的个性化系统，维护本地数据的隐私，并且只需要信任自己的设备而不是其他实体。因此，本项目探讨如何设计隐私方案，也提供良好的个性化学习性能，以及如何设计强大的协作方案，提供良好的个性化学习性能，尽管恶意的参与者。特别是，在个性化学习的隐私任务中，该项目计划探索个性化隐私机制的设计，这些机制对协作学习所需的迭代交互具有鲁棒性，并使用信息理论和统计工具分析其隐私性能权衡。在个性化学习的安全性任务中，该项目利用高维稳健统计和信息理论的思想来开发具有理论保证的稳健机制，以在存在恶意参与设备的情况下实现个性化学习，包括调查何时协作是有益的。该项目的成功完成将推动最新技术的发展，并通过正式的隐私和安全保证，在没有敌对计算假设的情况下，在信息理论和值得信赖的联邦/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进行评估，被认为值得支持。