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

联合学习和本地差异隐私--原始数据保留在设备上，只有经过清理的更新才会发送到中央服务器--使一些人工智能应用程序能够处理敏感数据，同时仍能保护用户隐私。该项目的目标是将联合学习的理论和实践从“单个用户贡献一个记录”推进到更广泛的网络环境中，其中多个用户连接到一个社交网络中。在这种情况下部署联合分析的主要挑战是需要平衡多个标准与隐私和准确性之间的权衡。这些是通信效率--因为客户端节点通常是低带宽、健壮性的--因为分布式和联网设置为对手敞开了大门，以及更复杂的隐私泄露，这可能是联网设置的结果。该项目的目标是通过结合统计学、隐私保护算法和图表算法的想法来应对这些挑战，并最终开发一套广泛和通用的算法，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来参与社区建设活动。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。

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.