Abstract
For data openness and sharing, we need to publish data and protect sensitive data at the same time. This paper provides the users with a system to realize privacy-preserving data publishing, which is implemented based on differential privacy. It has the following characteristics: (1) the raw data are first imported into a database and then are used to generate synthetic data for publishing; (2) a user can choose different privacy preservation levels for the synthetic data; (3) a subset of the attributes can been chosen to be synthesized while keeping the others untouched.
Cite
CITATION STYLE
Wang, Z., Zhu, Y., & Zhou, X. (2019). A Data Publishing System Based on Privacy Preservation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 553–556). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_85
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