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Jul, 2014
私有学习和加噪:纯差分隐私与近似差分隐私
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
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Amos Beimel, Kobbi Nissim, Uri Stemmer
TL;DR
该研究通过将真正的差分隐私和近似(ε,Δ)-差分隐私应用于优化问题中,研究比较了私有学习和消毒的样本复杂性,同时构建了用于高维中的点函数,阈值函数和轴对齐矩形的私有学习器以及标签私有学习,证明了VC 维完全刻画了学习带标签隐私的样本复杂性。
Abstract
We compare the
sample complexity
of
private learning
[Kasiviswanathan et al. 2008] and sanitization~[Blum et al. 2008] under pure $\epsilon$-
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