Please use this identifier to cite or link to this item: https://doi.org/10.1145/2063576.2063945
Title: Utility-driven anonymization in data publishing
Authors: Xue, M.
Karras, P.
Raïssi, C.
Pung, H.K. 
Keywords: anonymization
pattern-preserving
privacy
utility-driven
Issue Date: 2011
Source: Xue, M.,Karras, P.,Raïssi, C.,Pung, H.K. (2011). Utility-driven anonymization in data publishing. International Conference on Information and Knowledge Management, Proceedings : 2277-2280. ScholarBank@NUS Repository. https://doi.org/10.1145/2063576.2063945
Abstract: Privacy-preserving data publication has been studied intensely in the past years. Still, all existing approaches transform data values by random perturbation or generalization. In this paper, we introduce a radically different data anonymization methodology. Our proposal aims to maintain a certain amount of patterns, defined in terms of a set of properties of interest that hold for the original data. Such properties are represented as linear relationships among data points. We present an algorithm that generates a set of anonymized data that strictly preserves these properties, thus maintaining specified patterns in the data. Extensive experiments with real and synthetic data show that our algorithm is efficient, and produces anonymized data that affords high utility in several data analysis tasks while safeguarding privacy. © 2011 ACM.
Source Title: International Conference on Information and Knowledge Management, Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/41400
ISBN: 9781450307178
DOI: 10.1145/2063576.2063945
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