纵向逻辑回归中基于残差的标签保护机制
Residue-based Label Protection Mechanisms in Vertical Logistic Regression
AI总结：
针对纵向逻辑回归中利用残差变量推断私有标签的攻击，本文提出加性噪声、乘性噪声及融合本地差分隐私与同态加密的混合保护机制，并验证其高效且有效。
摘要：
联邦学习（FL）使分布式参与方能够协同训练全局模型，而无需彼此泄露其私有数据。近来，参与方持有相同样本集合但具有不同特征的纵向 FL 受到越来越多的关注。本文首先提出一种标签推断攻击方法，以研究纵向逻辑回归模型潜在的隐私泄露问题。具体而言，我们发现攻击者可以利用残差变量来推断私有的标签；这些残差变量通过求解由本地数据集和接收到的解密梯度所构建的线性方程组计算得到。为应对该问题，我们随后提出三种保护机制，即加性噪声机制、乘性噪声机制，以及结合本地差分隐私和同态加密技术的混合机制，以阻止该攻击并提高纵向逻辑回归模型的鲁棒性。实验结果表明，加性噪声机制和乘性噪声机制均能实现高效的标签保护，且模型测试准确率仅有轻微下降；此外，混合机制能够在不造成任何测试准确率下降的情况下实现标签保护，这证明了我们保护技术的有效性和高效性。
英文摘要：
Federated learning (FL) enables distributed participants to collaboratively learn a global model without revealing their private data to each other. Recently, vertical FL, where the participants hold the same set of samples but with different features, has received increased attention. This paper first presents one label inference attack method to investigate the potential privacy leakages of the vertical logistic regression model. Specifically, we discover that the attacker can utilize the residue variables, which are calculated by solving the system of linear equations constructed by local dataset and the received decrypted gradients, to infer the privately owned labels. To deal with this, we then propose three protection mechanisms, e.g., additive noise mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages local differential privacy and homomorphic encryption techniques, to prevent the attack and improve the robustness of the vertical logistic regression. model. Experimental results show that both the additive noise mechanism and the multiplicative noise mechanism can achieve efficient label protection with only a slight drop in model testing accuracy, furthermore, the hybrid mechanism can achieve label protection without any testing accuracy degradation, which demonstrates the effectiveness and efficiency of our protection techniques
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
