Using randomized response techniques for privacy-preserving data mining

Wenliang Du; Zhijun Zhan

doi:10.1145/956750.956810

Using randomized response techniques for privacy-preserving data mining

Wenliang Du, Zhijun Zhan

Department of Electrical Engineering & Computer Science

Research output: Contribution to conference › Paper › peer-review

244 Scopus citations

Abstract

Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy. We also show how the parameter used in the randomized response techniques affects the accuracy of the results.

Original language	English (US)
Pages	505-510
Number of pages	6
DOIs	https://doi.org/10.1145/956750.956810
State	Published - 2003
Event	9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States Duration: Aug 24 2003 → Aug 27 2003

Other

Other	9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Country/Territory	United States
City	Washington, DC
Period	8/24/03 → 8/27/03

Keywords

Data mining
Decision tree
Privacy
Security

ASJC Scopus subject areas

Software
Information Systems

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10.1145/956750.956810

Cite this

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abstract = "Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy. We also show how the parameter used in the randomized response techniques affects the accuracy of the results.",

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AU - Zhan, Zhijun

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AB - Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy. We also show how the parameter used in the randomized response techniques affects the accuracy of the results.

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KW - Decision tree

KW - Privacy

KW - Security

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Y2 - 24 August 2003 through 27 August 2003

ER -

Using randomized response techniques for privacy-preserving data mining

Abstract

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Keywords

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