BriefGPT.xyz
May, 2017
优先考虑准确度:选择不泄露隐私的差分隐私阈值以满足准确度约束的ERM
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
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Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Z. Steven Wu
TL;DR
本文提出了一种名为噪声约简框架的通用方法,能够应用于各种私有经验风险最小化(ERM)算法,使用它们来“搜索”隐私级别的空间,以找到在满足准确性约束下最强的隐私级别,并仅产生对搜索的隐私级别数量的对数开销。
Abstract
Traditional approaches to
differential privacy
assume a fixed privacy requirement $\epsilon$ for a computation, and attempt to maximize the accuracy of the computation subject to the
privacy constraint
. As
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