差分隐私与线性隐私的脂肪分解维度
Differential Privacy and the Fat-Shattering Dimension of Linear Queries
摘要：
在本文中，我们考虑在差分隐私的约束下回答线性查询的任务。这是一个通用的、经过充分研究的查询类，它捕获了其他常用的查询类，包括谓词查询和直方图查询。我们表明，一组线性查询可以回答的准确性是密切相关的脂肪粉碎尺寸，一个属性，其特点是学习的实值函数在不可知学习设置。
英文摘要：
In this paper, we consider the task of answering linear queries under the constraint of differential privacy. This is a general and well-studied class of queries that captures other commonly studied classes, including predicate queries and histogram queries. We show that the accuracy to which a set of linear queries can be answered is closely related to its fat-shattering dimension, a property that characterizes the learnability of real-valued functions in the agnostic-learning setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
