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Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing

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Faculty of Computer Science, Universitas Indonesia, West Java 16424, Indonesia
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Department of Informatics Education, Universitas Negeri Jakarta, Jl.Rawamangun Muka, Jakarta 13220, Indonesia
*
Author to whom correspondence should be addressed.
Information 2019, 10(12), 362; https://doi.org/10.3390/info10120362
Received: 15 October 2019 / Revised: 12 November 2019 / Accepted: 13 November 2019 / Published: 21 November 2019
Investigation into privacy preserving data publishing with multiple sensitive attributes is performed to reduce probability of adversaries to guess the sensitive values. Masking the sensitive values is usually performed by anonymizing data by using generalization and suppression techniques. A successful anonymization technique should reduce information loss due to the generalization and suppression. This research attempts to solve both problems in microdata with multiple sensitive attributes. We propose a novel overlapped slicing method for privacy preserving data publishing with multiple sensitive attributes. We used discernibility metrics to measure information loss. The experiment result shows that our method obtained a lower discernibility value than other methods. View Full-Text
Keywords: privacy preserving data publishing; overlapped slicing; multiple sensitive attributes; discernibility privacy preserving data publishing; overlapped slicing; multiple sensitive attributes; discernibility
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MDPI and ACS Style

Widodo; Budiardjo, E.K.; Wibowo, W.C. Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing. Information 2019, 10, 362.

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