联邦学习中的梯度泄露风险与理论防御机制研究 -计算机科学与技术专业

摘要

联邦学习作为一种新兴的分布式机器学习范式，能够在保护数据隐私的同时实现模型训练，但其安全性仍面临诸多挑战，尤其是梯度泄露问题可能暴露敏感信息。本研究聚焦于联邦学习中梯度泄露的风险分析与理论防御机制设计，旨在构建更加安全和可靠的联邦学习框架。研究首先通过系统化的实验验证了梯度信息在联邦学习中的潜在泄露风险，表明攻击者可通过逆向分析或差分攻击恢复部分原始数据。所提方法能够在多种场景下显著提升数据隐私保护水平，同时保持较高的模型收敛速度和预测精度。此外，本研究还从理论角度分析了防御机制的鲁棒性，并提出了量化评估隐私泄露风险的指标体系。主要贡献在于首次将差分隐私与加密技术深度融合应用于联邦学习领域，为解决梯度泄露问题提供了新思路，同时为后续研究奠定了理论基础。

关键词：联邦学习；梯度泄露；差分隐私；加密技术；隐私保护

Gradient Leakage Risks and Theoretical Defense Mechanisms in Federated Learning

英文人名

Directive teacher:×××

Abstract

Federated learning, as a new distributed machine learning paradigm, can realize model training while protecting data privacy, but its security still faces many challenges, especially the gradient leakage problem that may expose sensitive information. This research focuses on the risk analysis and theoretical defense mechanism design of gradient leakage in federated learning, aiming to build a more secure and reliable federated learning fr amework. This paper firstly verifies the potential leakage risk of gradient information in federation learning through systematic experiments, and shows that attackers can recover part of the original data through reverse analysis or differential attacks. The proposed method can significantly improve the level of data privacy protection under various scenarios, while maintaining high model convergence speed and prediction accuracy. In addition, this study also analyzes the robustness of the defense mechanism from a theoretical point of view, and puts forward a quantitative index system to assess the risk of privacy disclosure. The main contribution is that the deep integration of differential privacy and encryption technology is applied to the federated learning field for the first time, which provides a new idea for solving the gradient leakage problem and lays a theoretical foundation for the subsequent research.

Keywords: Federal Learning；Gradient Leakage；Differential Privacy；Encryption Technology；Privacy Protection

引言 1

一、联邦学习基础与梯度泄露风险概述 1

(一)联邦学习的基本原理 1

(二)梯度泄露的风险来源 2

(三)当前研究现状与挑战 2

二、梯度泄露的攻击模型与威胁分析 3

(一)攻击模型的分类与特点 3

(二)基于梯度的隐私泄露机制 3

(三)实际场景中的威胁评估 4

三、理论防御机制的设计与优化 4

(一)防御机制的核心原则 4