功能恢复下的列表隐私

IF 2.2 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Information Theory Pub Date : 2024-07-01 DOI:10.1109/TIT.2024.3420892
Ajaykrishnan Nageswaran;Prakash Narayan
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引用次数: 0

摘要

对于用户数据的给定函数,查询者必须根据用户提供的查询响应,以至少规定的概率恢复函数值。根据这一要求,用户生成查询响应时,要尽量减少查询者根据查询响应猜出数据值所属的规定大小列表的可能性。我们得到了最大列表隐私的一般反向上界。通过涉及添加噪声查询响应的显式可实现性方案,我们证明在二值函数的情况下,这个界限是紧密的。
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List Privacy Under Function Recoverability
For a given function of user data, a querier must recover with at least a prescribed probability, the value of the function based on a user-provided query response. Subject to this requirement, the user forms the query response so as to minimize the likelihood of the querier guessing a list of prescribed size to which the data value belongs based on the query response. We obtain a general converse upper bound for maximum list privacy. This bound is shown to be tight for the case of a binary-valued function through an explicit achievability scheme that involves an add-noise query response.
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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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