K-Anonymity Multidimensional Suppression

International Journal of Computer Science (IJCS Journal) Published by SK Research Group of Companies (SKRGC) Scholarly Peer Reviewed Research Journals

Format: Volume 2, Issue 1, No 1, 2014.

Copyright: All Rights Reserved ©2014

Year of Publication: 2014

Author: P.PONSEKAR,S.R.SARANYA

Reference:IJCS-031

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Abstract

Knowledge Discovery in Databases (KDDs) is the process of identifying valid, novel, useful, and understandable patterns from large data sets. Data Mining is the core of the KDD process, involving algorithms that explore the data, develop models, and discover significant patterns. Data mining has emerged as a key tool for a wide variety of applications, ranging from national security to market analysis. Many of these applications involve mining data that include private and sensitive information about users. Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being released for data mining. One way to anonymize data set is to manipulate its content so that the records adhere to k-anonymity. Two common manipulation techniques used to achieve k-anonymity of a data set are generalization and suppression. Generalization refers to replacing a value with a less specific but semantically consistent value, while suppression refers to not releasing a value at all. Generalization is more commonly applied in this domain since suppression may dramatically reduce the quality of the data mining results if not properly used.

References

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Keywords

Privacy-preserving data mining, k-anonymity, deindentified data, decision trees.

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