Differential Privacy via Weighted Sampling Set Cover
Zhonglian Hu, Zhaobin Liu, Yangyang Xu, Zhiyang Li · International Journal of Security and Its Applications · 2016
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 lowdimensional 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.