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Privacy-Preserving Data Publishing: A Survey on Recent Developments
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BibTeX
@MISC{Fung_privacy-preservingdata,
author = {Benjamin C. M. Fung and Ke Wang and Rui Chen and Philip S. Yu},
title = {Privacy-Preserving Data Publishing: A Survey on Recent Developments},
year = {}
}
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Abstract
The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published, and agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions.
Keyphrases
privacy-preserving data publishing recent development various party different approach propose future research direction current practice useful information insufficient protection distinguish ppdp considerable attention practical data publishing different data publishing scenario tremendous opportunity sensitive information information-based decision making excessive data distortion individual privacy data privacy many approach certain data digital information research community related problem original form mutual benefit
