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

SaTC：核心：小型：网络数据的稳健且私密的联合分析

基本信息

批准号：
2241100
负责人：
Kamalika Chaudhuri
金额：
$ 60万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241100&HistoricalAwards=false
关键词：
SaTC CORE Small Robust Private

项目摘要

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.

具有本地差异隐私的联合学习（原始数据保留在设备上，并且只有经过净化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人工智能应用程序，同时仍然保护了用户隐私。该项目的目标是将联邦学习的理论和实践推进到超越“单个用户贡献记录”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需要平衡多个标准以及隐私准确性权衡。这些是通信效率——因为客户端节点通常是低带宽、鲁棒性——因为分布式和网络设置给对手留下了大门，以及更复杂的隐私泄露，这可能是由于网络设置而出现的。该项目的目标是通过结合统计、隐私保护算法以及图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，用于网络设置中的私有且强大的联合分析。项目团队还将通过组织隐私研讨会以及在高中的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价值和更广泛的影响审查标准进行评估，认为值得支持。