提出一种隐私保护的多分类SVM，仅访问数据一次即可实现高精度分类。
Multi-Class Support Vector Machine with Differential Privacy
- 采用一次性构建多分类边界的方法，避免重复查询数据
- 通过权重与梯度扰动实现差分隐私，保障数据安全
- 在多分类任务中性能优于现有隐私保护SVM方法
随着机器学习中数据隐私保护需求的增长，差分隐私（DP）成为构建隐私保护模型的重要框架。支持向量机（SVM）因其稳健的间隔保证和在二分类中的出色表现而被广泛使用。然而，将差分隐私应用于多分类SVM仍不充分，因为标准的一对多（OvR）和一对一（OvO）方法在构建多个二分类器时会反复查询每个数据样本，导致隐私预算随类别数线性消耗。为克服这一限制，我们探索了全一体系的多分类SVM方法，仅需一次访问每个数据样本即可构造具有最大间隔特性的多分类边界。我们提出了一种新型的差分隐私多分类SVM（PMSVM），结合权重扰动与梯度扰动策略，并提供严格的敏感性与收敛性分析，确保全一体系下的差分隐私。实验结果表明，该方法在多分类场景中显著优于现有DP-SVM方法。
原文摘要 · Abstract (English)
With the increasing need to safeguard data privacy in machine learning models, differential privacy (DP) is one of the major frameworks to build privacy-preserving models. Support Vector Machines (SVMs) are widely used traditional machine learning models due to their robust margin guarantees and strong empirical performance in binary classification. However, applying DP to multi-class SVMs is inadequate, as the standard one-versus-rest (OvR) and one-versus-one (OvO) approaches repeatedly query each data sample when building multiple binary classifiers, thus consuming the privacy budget proportionally to the number of classes. To overcome this limitation, we explore all-in-one SVM approaches for DP, which access each data sample only once to construct multi-class SVM boundaries with margin maximization properties. We propose a novel differentially Private Multi-class SVM (PMSVM) with weight and gradient perturbation methods, providing rigorous sensitivity and convergence analyses to ensure DP in all-in-one SVMs. Empirical results demonstrate that our approach surpasses existing DP-SVM methods in multi-class scenarios.
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