Different strategies for differentially private histogram publication

Abstract

Differential privacy is a strong notion for protecting individual privacy in data analysis or publication, with strong privacy guaranteeing security against adversaries with arbitrary background knowledge. A histogram is a representative and popular tool for data publication and visualization tasks. Following the emergence and development of data analysis and increasing release demands, protecting the private data and preventing sensitive information from leakage has become one of the major challenges for histogram publication. In recent years, many approaches have been proposed for publishing histograms with differential privacy. This paper explores the problem of publishing histograms with differential privacy, and provides a systematical summarization of existing research efforts in this field, begining with a discussion of the basic principles and characteristics of the technology. Furthermore, we provide a comprehensive comparison of a series of state-of-the-art histogram publication schemes. Finally, we provide possible suggestions for further expansions of future work in this area.