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Differentially private data publishing and analysis: a survey
journal contribution
posted on 2017-08-01, 00:00 authored by Tianqing Zhu, Gang LiGang Li, Wanlei Zhou, P S YuDifferential 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.
History
Journal
IEEE Transactions on Knowledge and Data EngineeringVolume
29Issue
8Pagination
1619 - 1638Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
Link to full text
ISSN
1041-4347eISSN
1558-2191Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
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Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsEngineering, Electrical & ElectronicComputer ScienceEngineeringDifferential privacyprivacy preserving data publishingprivacy preserving data analysisPUBLICATIONCOMPLEXITYSYSTEMSALGORITHMSFRAMEWORKQUERIESData Format
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