SaTC: CORE: Small: Robust and Private Federated Analytics on Networked Data

具有本地差异隐私的联合学习-原始数据保留在设备上，只有经过消毒的更新才会发送到中央服务器-已经在敏感数据上启用了许多AI应用程序，同时仍然保护用户隐私。该项目的目标是将联邦学习的理论和实践从“单个用户贡献记录”扩展到更广泛的网络环境，其中多个用户连接到社交网络。在这种情况下部署联合分析的主要挑战是，有多个标准需要与隐私准确性权衡在一起。这些是通信效率-因为客户端节点通常是低带宽的，鲁棒性-因为分布式和网络设置为对手打开了大门，以及更复杂的隐私泄露，这可能是网络设置的结果。该项目的目标是通过结合统计、隐私保护算法和图形算法的思想来应对这些挑战，并最终开发一套广泛而通用的算法，用于网络环境中的私有和强大的联合分析。该项目团队还将通过组织隐私研讨会和高中外展活动参与社区建设活动。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。

Federated learning with local differential privacy -- where raw data stays on device, and only sanitized updates are sent to a central server -- has enabled a number of AI applications on sensitive data while still preserving user privacy. The goal of this project is to advance the theory and practice of federated learning beyond "a single user contributing a record" to a broader networked setting where multiple users are connected into a social network. The main challenge in deploying federated analytics in this setting is that there are multiple criteria that need to balanced together with the privacy-accuracy tradeoff. These are communication efficiency -- as the client nodes are typically low-bandwidth, robustness -- as the distributed and networked setting leaves the door open to adversaries, and more complex privacy leaks, which could arise as a result of the networked setting. The goal of the project is to address these challenges by combining ideas from statistics, privacy-preserving algorithms as well as graph algorithms and ultimately developing a broad and general suite of algorithms for private and robust federated analytics in networked settings. The project team will also engage in community-building activities by organizing privacy workshops, as well as outreach activities in high schools.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.