期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：2
页码：487-495
出版社：Shri Pannalal Research Institute of Technolgy
摘要：In privacy preserving data mining (PPDM) field of research studies how knowledge or patterns can be extracted from large data stores while maintaining commercial or legislative privacy constraints. Association rules (frequent itemsets), classification and clustering are main methods used in data mining research. One of the great challenges of data mining is finding hidden patterns without violating data owners' privacy. Privacy preserving data mining came into prominence as a solution. In the aim of the paper, Matrix Apriori algorithm is modified and a frequent itemset hiding framework is developed. Four frequent itemset hiding algorithms are proposed such that: first all versions work without pre-mining so privacy breech caused by the knowledge obtained by finding frequent itemsets is prevented in advance, secondly efficiency is increased since no pre-mining is required, thirdly supports are found during hiding process and at the end sanitized dataset and frequent itemsets of this dataset are given as outputs so no post- mining is required, finally the heuristics use pattern lengths rather than transaction lengths eliminating the possibility of distorting more valuable data.
关键词：Data Mining; Frequent Item Mining; ; Data Hiding; PPDM
