2011Unpublished venueRequires access

Evolution of privacy-preserving data publishing

Yongbin Yuan, Jing Yang, Jianpei Zhang, Sheng Lan, Junwei Zhang

Open publisher page 5 citations

Abstract

To achieve privacy protection better in data publishing, data must be sanitized before release. Research on protecting individual privacy and data confidentiality has received contributions from many fields. In order to grasp the development of privacy preserving data publishing, we discussed the evolution of this theme, focused on privacy mechanism, data utility and its metrics. The privacy mechanism, such as k-anonymity, l-diversity and t-closeness, provides formal safety guarantees and data utility preserve useful information while publishing data. Meantime, we discussed social network privacy and location based service. Finally, we made a conclusion with respect to privacy preserving data publishing, and given further research directions.

About this research paper

What this paper is about

To achieve privacy protection better in data publishing, data must be sanitized before release. Research on protecting individual privacy and data confidentiality has received contributions from many fields. In order to grasp the development of privacy preserving data publishing, we discussed the evolution of this theme, focused on privacy mechanism, data utility and its metrics. The privacy mechanism, such as k-anonymity, l-diversity and t-closeness, provides formal safety guarantees and data utility preserve useful information while publishing data. Meantime, we discussed social network privacy and location based service. Finally, we made a conclusion with respect to privacy preserving data publishing, and given further research directions.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Main findings

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Available abstract

To achieve privacy protection better in data publishing, data must be sanitized before release. Research on protecting individual privacy and data confidentiality has received contributions from many fields. In order to grasp the development of privacy preserving data publishing, we discussed the evolution of this theme, focused on privacy mechanism, data utility and its metrics. The privacy mechanism, such as k-anonymity, l-diversity and t-closeness, provides formal safety guarantees and data utility preserve useful information while publishing data. Meantime, we discussed social network privacy and location based service. Finally, we made a conclusion with respect to privacy preserving data publishing, and given further research directions.

Key concepts: Data publishing, Computer science, Information privacy, Publishing, Internet privacy, k-anonymity, Anonymity, Data anonymization

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