English(EN) Quadratic Objective Perturbation: Curvature-Based Differential Privacy

新的二次目标扰动方法增强了机器学习的差分隐私

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员推出了一种名为二次目标扰动（QOP）的新型机器学习差分隐私方法。与需要有界梯度的线性目标扰动（LOP）不同，QOP使用随机二次型来诱导强凸性和稳定性。这种方法允许在较弱的假设下提供隐私保证，即使在插值状态下也是如此，并且与近似解兼容。 AI

影响引入了一种新的隐私保护技术，可以使机器学习模型在敏感数据环境中得到更广泛的应用。

排序理由这是一篇介绍机器学习差分隐私新理论方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Daniel Cortild, Coralia Cartis · 2026-05-08 04:00

二次目标扰动：基于曲率的差分隐私

arXiv:2605.05905v1 Announce Type: new Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization. In particular, Linear Objective Perturbation (LOP) enforces privacy by adding a random linear term, while strong convexity and sta…