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<p>The support vector machine, a widely used binary classification method, may expose sensitive information during training. To address this, the authors propose a personalized differential privacy method that extends differential privacy. Specifically, the authors introduce personalized differentially private support vector machines to meet different individuals’ privacy requirements, using a reweighting strategy and the Laplace mechanism. Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels. Extensive experiments demonstrate that the proposed methods outperform the existing methods.</p>

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Personalized Differential Privacy for Support Vector Machines

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Xiaofeng Wang,
  • Xingwei Liu,
  • Wangli Xu

摘要

The support vector machine, a widely used binary classification method, may expose sensitive information during training. To address this, the authors propose a personalized differential privacy method that extends differential privacy. Specifically, the authors introduce personalized differentially private support vector machines to meet different individuals’ privacy requirements, using a reweighting strategy and the Laplace mechanism. Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels. Extensive experiments demonstrate that the proposed methods outperform the existing methods.