%0 Journal Article %1 Fung:2010:PDP:1749603.1749605 %A Fung, Benjamin C. M. %A Wang, Ke %A Chen, Rui %A Yu, Philip S. %C New York, NY, USA %D 2010 %I ACM %J ACM Comput. Surv. %K pseudo %N 4 %P 14:1--14:53 %R 10.1145/1749603.1749605 %T Privacy-preserving data publishing: A survey of recent developments %U http://doi.acm.org/10.1145/1749603.1749605 %V 42 %X 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 on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. <i>Privacy-preserving data publishing</i> (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.

@article{Fung:2010:PDP:1749603.1749605, 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 on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. <i>Privacy-preserving data publishing</i> (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.}, acmid = {1749605}, added-at = {2019-03-08T09:27:13.000+0100}, address = {New York, NY, USA}, articleno = {14}, author = {Fung, Benjamin C. M. and Wang, Ke and Chen, Rui and Yu, Philip S.}, biburl = {https://www.bibsonomy.org/bibtex/2076319ebf0f40ffa5c906ca6216079a5/dschiffner}, description = {Privacy-preserving data publishing}, doi = {10.1145/1749603.1749605}, interhash = {e201825beb91f1be85bbf05eb97aea2a}, intrahash = {076319ebf0f40ffa5c906ca6216079a5}, issn = {0360-0300}, issue_date = {June 2010}, journal = {ACM Comput. Surv.}, keywords = {pseudo}, month = jun, number = 4, numpages = {53}, pages = {14:1--14:53}, publisher = {ACM}, timestamp = {2019-03-08T09:27:13.000+0100}, title = {Privacy-preserving data publishing: A survey of recent developments}, url = {http://doi.acm.org/10.1145/1749603.1749605}, volume = 42, year = 2010 }