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Modified Anonymity Model for Privacy Preserving Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 64 - Number 17 |
| Year of Publication: 2013 |
| Authors: P. Usha, R. Shriram, W. Aisha Banu |
10.5120/10728-5683
|
P. Usha, R. Shriram, W. Aisha Banu . Modified Anonymity Model for Privacy Preserving Data Mining. International Journal of Computer Applications. 64, 17 ( February 2013), 26-32. DOI=10.5120/10728-5683
Abstract
Data Mining plays a vital role in today's information-oriented world where it has been widely applied in various organizations. The current trend is that organizations need to share data for mutual benefit. This has led to a lot of concern over privacy in the recent years. It has also raised a potential threat of revealing sensitive data of an individual when the data is released publicly. Various methods have been proposed to tackle the privacy preservation problem. But the recurring problem is information loss. The loss of sensitive information about certain individuals may affect the data quality and in extreme cases the data may become completely useless. In recent years Privacy preserving data mining has emerged as a key domain of research. One of the methods used for preserving privacy is k-anonymization. k-anonymity demands that every tuple in the dataset released be indistinguishably related to no fewer than k respondents. But the distribution preservation is not guaranteed. In this work a modified k-anonymity model is introduced where the privacy in a dataset is preserved while preserving the distribution also.
References
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