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

个性化推荐系统已被广泛部署，并通过Web和移动应用程序进行集中数据收集，取得了巨大成功。与此同时，它们也引发了公众对隐私的重大和合理的担忧。社会正在跨越一条新的边界，从更简单的物联网设备(传感)到(半)自动驾驶车辆(汽车、无人机)，数百万台设备通过网络连接在一起。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，即如何设计具有信息论隐私和安全保障的个性化学习模型。这一项目的研究成果将通过出版物广泛传播，包括国际和平研究所参与教学和与产业界的互动。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，本项目探索如何设计具有良好个性化学习性能的隐私方案，以及如何设计健壮的协作方案，使其在恶意参与者的情况下仍具有良好的个性化学习性能。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健壮性，并使用信息论和统计工具分析其隐私与性能之间的权衡。在个性化学习的安全任务中，该项目利用高维稳健统计和信息理论的想法来开发具有理论保证的稳健机制，以便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进最先进的技术，并通过正式的隐私和安全保证，在信息理论和可信赖的联邦/分布式机器学习和优化之间架起桥梁，而不存在敌对的计算假设。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.