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.

使用局部差别隐私的联合学习 - 原始数据停留在设备上，并且仅将卫生更新发送到中央服务器 - 在仍然保留用户隐私的同时，启用了许多AI应用程序。该项目的目的是将联合学习的理论和实践推进到“单一用户为记录贡献记录”之外，以将多个用户连接到社交网络中。在这种情况下，部署联邦分析的主要挑战是，有多个标准需要与隐私 - 准确性的权衡保持平衡。这些是通信效率 - 由于客户节点通常是低频带宽度，稳健性的 - 随着分布式和网络设置使对手开门，并且更复杂的隐私泄漏，这可能是由于网络设置而引起的。该项目的目的是通过结合统计数据，保护隐私的算法以及图形算法的想法，并最终开发出广泛而一般的算法套件，以解决网络设置中的私人和强大的联合分析。该项目团队还将通过组织隐私研讨会以及高中宣传活动来从事社区建设活动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估的评估来支持的。