Collaborative Research: SaTC: EAGER: Trustworthy and Privacy-preserving Federated Learning

协作研究：SaTC：EAGER：值得信赖且保护隐私的联邦学习

基本信息

批准号：
2140477
负责人：
My Thai
金额：
$ 6万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140477&HistoricalAwards=false
关键词：
Collaborative Research SaTC EAGER Trustworthy

项目摘要

Researchers and the public have been alarmed by a fact that user privacy of training data in machine learning (ML) models has been exploited in many ways, leading to a rapidly expanding field of federated learning(FL). In FL, the learning of ML models is performed directly on user devices, while the aggregated model is composed with a help of a central server. As data never leave user devices, this new paradigm offers a key promise to protect data privacy. It, unfortunately, poses new challenges in both security and privacy. On one hand, malicious users can compromise security by injecting backdoors into the model updates, thus poisoning the aggregated model. On the other hand, there is a risk of privacy leakage as an untrusted server can inverse the model update to expose private data. This project develops a principled and systematic FL framework that simultaneously offers both privacy and security protection against threats from malicious users and servers. As part of this project, novel protocols will be developed to ensure verifiability, execution integrity, model confidentiality, and protection against adversarial attacks. The success of the project holds significant potential in expanding machine learning to new application scenarios, especially, when no trust is assumed among the stakeholders. The findings may also benefit other fields, such as zero-knowledge proof, distributed machine learning, and distributed ledger technology. The project involves students at all levels, with an emphasis on attracting students from underrepresented groups and K-12 students.The focus of the project is to develop a principled and systematic FL framework with three jointly key components: 1) a lightweight secure aggregation and backdoor inspection mechanisms in which each user is responsible for both securely aggregating their values and an attestation of an attack-free model, 2) a succinct non-interactive argument of knowledge (SNARK) attestation that minimizes non-arithmetic operations to maintain both high accuracy and communication-efficiency, and 3) a blockchain-based FL architecture to tight together security measures at various stages in the training process, offering privacy and security protection for the entire training process. By shifting a task of proving that model is free-of-attack to users, coupling of Blockchain for transparency, this project provides a first step towards a secured and privacy protection of distributed learning systems. The success of this novel approach will significantly impact the design of FL for many real-life applications.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.

研究人员和公众对机器学习中培训数据（ML）模型中的用户隐私已通过多种方式进行了震惊，从而导致了迅速扩展的联邦学习领域（FL）。在FL中，ML模型的学习直接在用户设备上执行，而汇总模型则在中央服务器的帮助下组成。由于数据永远不会离开用户设备，因此这个新范式提供了保护数据隐私的关键承诺。不幸的是，它在安全和隐私方面构成了新的挑战。一方面，恶意用户可以通过将后门注入模型更新来损害安全性，从而使汇总模型中毒。另一方面，存在隐私泄漏的风险，因为不受信任的服务器可以反向模型更新以暴露私人数据。该项目开发了一个有原则的系统的FL框架，同时为恶意用户和服务器的威胁提供隐私和安全保护。作为该项目的一部分，将开发新的协议，以确保可验证性，执行完整性，模型机密性和防止对抗性攻击的保护。该项目的成功具有将机器学习扩展到新的应用程序方案的巨大潜力，尤其是在利益相关者中没有信任时。这些发现还可能使其他领域受益，例如零知识证明，分布式机器学习和分布式分类帐技术。该项目涉及各级学生，重点是吸引来自代表性不足的小组和K-12学生的学生。该项目的重点是开发一个具有三个共同关键组成部分的原则性和系统的FL框架：1）轻巧的安全聚合和一个轻巧的安全汇总和后门检查机制，每个用户都负责安全地汇总其价值和对无攻击模型的证明，2）简洁的非相互作用的知识论证（SNARK）证明（SNARK）证明，以最大程度地降低非偏心操作以保持高精度和沟通效率以及3）基于区块链的FL体系结构，在培训过程中的各个阶段将安全措施整合在一起，为整个培训过程提供隐私和安全保护。通过转移一项证明模型对用户的攻击，区块链的透明度耦合的任务，该项目为分布式学习系统的安全隐私保护提供了第一步。这种新颖的方法的成功将对许多现实生活应用的设计产生重大影响。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。