Issue No. 08 - Aug. (2017 vol. 29)
Tianqing Zhu , School of Information Technology, Deakin University, Burwood, Australia
Gang Li , School of Information Technology, Deakin University, Burwood, Australia
Wanlei Zhou , School of Information Technology, Deakin University, Burwood, Australia
Philip S. Yu , Department of Computer Science, University of Illinois at Chicago, Chicago, IL
Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.
Privacy, Data privacy, Publishing, Data analysis, Sensitivity, Algorithm design and analysis, Data models
T. Zhu, G. Li, W. Zhou and P. S. Yu, "Differentially Private Data Publishing and Analysis: A Survey," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 8, pp. 1619-1638, 2017.