Differential Privacy via Weighted Sampling Set Cover

Abstract

Differential privacy is a security guarantee model which widely used in privacy preserving data publishing, but the query result can’t be used in data research directly, especially in high-dimensional datasets. To address this problem, we propose a dimensionality reduction method. The core idea of this method is using a series of low-dimensional datasets to reconstruct a high-dimensional dataset, it improves data availability eventually. The main issue of this method is the reconstruction integrity, so a special sampling via set cover model is proposed in this article, which builds a multidimensional composite marginal tables set as a new middleware in differential privacy model. As a result, any form of disjunctive queries can be answered, and the accuracy of data query is improved. The experiment results also show the effectiveness of our method in practice.