隐私放大和去耦无需平滑

IF 2.2 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Information Theory Pub Date : 2023-08-28 DOI:10.1109/TIT.2023.3301812
Frédéric Dupuis
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引用次数: 19

摘要

我们用$\alpha \in(1,2]$)阶的R\ enyi熵证明了隐私放大和解耦的可实现性结果;这扩展了之前的结果,适用于$\alpha=2$。这个证明在$\alpha$接近1的情况下有效,这意味着我们可以在许多应用中绕过平滑最小熵,其中边界来自全量子AEP或熵积累,并使用R\ enyi熵进行整个证明,从而很容易获得最终任务的误差指数。这有效地取代了平滑,这是一个困难的高维优化问题,通过单个实参数$\alpha$的优化问题。
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Privacy Amplification and Decoupling Without Smoothing
We prove an achievability result for privacy amplification and decoupling in terms of the sandwiched Rényi entropy of order $\alpha \in (1,2]$ ; this extends previous results which worked for $\alpha =2$ . The fact that this proof works for $\alpha $ close to 1 means that we can bypass the smooth min-entropy in the many applications where the bound comes from the fully quantum AEP or entropy accumulation, and carry out the whole proof using the Rényi entropy, thereby easily obtaining an error exponent for the final task. This effectively replaces smoothing, which is a difficult high-dimensional optimization problem, by an optimization problem over a single real parameter $\alpha $ .
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来源期刊
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory 工程技术-工程:电子与电气
CiteScore
5.70
自引率
20.00%
发文量
514
审稿时长
12 months
期刊介绍: The IEEE Transactions on Information Theory is a journal that publishes theoretical and experimental papers concerned with the transmission, processing, and utilization of information. The boundaries of acceptable subject matter are intentionally not sharply delimited. Rather, it is hoped that as the focus of research activity changes, a flexible policy will permit this Transactions to follow suit. Current appropriate topics are best reflected by recent Tables of Contents; they are summarized in the titles of editorial areas that appear on the inside front cover.
期刊最新文献
Table of Contents IEEE Transactions on Information Theory Publication Information IEEE Transactions on Information Theory Information for Authors Large and Small Deviations for Statistical Sequence Matching Derivatives of Entropy and the MMSE Conjecture
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