Differential Privacy and the Fat-Shattering Dimension of Linear Queries

Details Differential Privacy and the Fat-Shattering Dimension of Linear Queries Differential Privacy and the Fat-Shattering Dimension of Linear Queries

2010-04-19

arxiv.org/abs/1004.3205v2

Computer Science

Data Structures and Algorithms

Appears in APPROX 2010

Scientific paper

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.

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