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Apr, 2024
差分隐私线性模型在高维数据上的综述
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
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Amol Khanna, Edward Raff, Nathan Inkawhich
TL;DR
通过综合评估不同的优化方法，本研究对高维度差分隐私线性模型的优化方法进行了广泛的回顾，并表明稳健且坐标优化的算法表现最佳，从而为未来的研究提供了重要参考。
Abstract
linear models
are ubiquitous in data science, but are particularly prone to
overfitting
and
data memorization
in high dimensions. To guara
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