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.
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This work is supported by the National Nature Science Foundation of China (Nos. 61672408, 61472298), the National High Technology Research and Development Program (“863” Program) (No. 2015AA016007), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2015JQ6227) and China 111 Project (No. B16037).
Xue Meng received B.Eng. from Xi'an University of Post and Telecommunications in 2014. She is currently a graduate student in the School of Cyber Engineering at Xidian University. Her research insterests include privacy preserving data management and differentially private data publication.
Hui Li [corresponding author] received the B.Eng. from the Harbin Institute of Technology in 2005 and Ph.D. degree from Nanyang Technological University, Singapore in 2012, respectively. He is an associate professor in the School of Cyber Engineering, Xidian University, China. His research interests include data mining, knowledge management and discovery and privacy-preserving queries and analysis in big data. He has over 30 publications in data management research, the majority of which appear in toptier venues such as SIGMOD, SIGKDD, VLDB, ICDE, INFO-COM, TKDE and VLDB Journal.
Jiangtao Cui received the M.S. and Ph.D. degrees both in computer science, from Xidian University, Xi’an, China in 2001 and 2005 respectively. Between 2007 and 2008, he was with the Data and Knowledge Engineering group working on high-dimensional indexing for large scale image retrieval, at the University of Queensland (Australia). He is currently a professor in the School of Cyber Engineering, Xidian University, China. His current research interests include data and knowledge engineering, and high-dimensional indexing.
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Meng, X., Li, H. & Cui, J. Different strategies for differentially private histogram publication. J. Commun. Inf. Netw. 2, 68–77 (2017). https://doi.org/10.1007/s41650-017-0014-x
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DOI: https://doi.org/10.1007/s41650-017-0014-x