NDLI: Privacy preserving in data mining - Experimental research on SMEs data

Content Provider	IEEE Xplore Digital Library
Author	Grljevic, O. Bosnjak, Z. Mekovec, R.
Copyright Year	2011
Description	Author affiliation: University of Novi Sad, Faculty of Economics Subotica, Serbia (Grljevic, O.; Bosnjak, Z.) \|\| University of Zagreb, Faculty of Organization and Informatics, Varaždin, Croatia (Mekovec, R.)
Abstract	Analysis of data on individuals and business sensitive data as well as revealing the results of such analysis without disclosing confidential and sensitive information is a very important issue. Many techniques for preserving privacy of data are currently being used. This paper is addressing some of the basic techniques: randomization, k-anonymity, distributed privacy preserving and application effectiveness downgrading. Most of the techniques should be applied in the phase of data collection or their preprocessing, which can lead to different results (better or worse) of data mining than would be obtained on original data. For this reason, data analysts should be encouraged to quantify the ratio between privacy preserved in data with application of each technique and the loss of data or quality of outputs. This paper illustrates the application of certain techniques for preserving privacy on experimental dataset, and reveals the effects that their use has on the results.
Starting Page	477
Ending Page	481
File Size	439737
Page Count	5
File Format	PDF
ISBN	9781457719752
e-ISBN	9781457719745
DOI	10.1109/SISY.2011.6034375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-08
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Privacy Noise Investments Companies
Content Type	Text
Resource Type	Article

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