Learning decision trees from anonymized data

Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 and Beckford, Jasmin 2017. Learning decision trees from anonymized data. Presented at: 8th Annual International Conf on ICT: Big Data, Cloud and Security, Singapore, 21-22 August 2017.

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Abstract

There is much interest in developing solutions for protecting data privacy in recent years, and many privacy models and data sanitization methods have been proposed. However, relatively little has been done to understand how existing data analysis techniques may be adapted to work with sanitized data. In this paper we report a study on learning decision trees from anonymized data. We sanitize data using the Mondrian algorithm to satisfy k-anonymity and adapt the ID3 algorithm to learn decision trees from sanitized data. Our preliminary experiments show that accurate decision trees can be learnt from anonymized data, and degradation of classification accuracy is no more than 2% with typical settings.

Item Type:	Conference or Workshop Item (Paper)
Status:	Unpublished
Schools:	Computer Science & Informatics
Date of First Compliant Deposit:	19 September 2017
Last Modified:	03 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/104823

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