H.3.14. Knowledge Management
M. Sakenian Dehkordi; M. Naderi Dehkordi
Abstract
Due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. Association Rules Mining is one of the data mining techniques to extract useful patterns in the form of association rules. One of the main problems in applying this technique on ...
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Due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. Association Rules Mining is one of the data mining techniques to extract useful patterns in the form of association rules. One of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. Hiding the association rules is one of the methods to preserve privacy and it is a main subject in the field of data mining and database security, for which several algorithms with different approaches are presented so far. An algorithm to hide sensitive association rules with a heuristic approach is presented in this article, where the Perturb technique based on reducing confidence or support rules is applied with the attempt to remove the considered item from a transaction with the highest weight by allocating weight to the items and transactions. Efficiency is measured by the failure criteria of hiding, number of lost rules and ghost rules, and execution time. The obtained results of this study are assessed and compared with two known FHSAR and RRLR algorithms, based on two real databases (dense and sparse). The results indicate that the number of lost rules in all experiments are reduced by 47% in comparison with RRLR and reduced by 23% in comparison with FHSAR. Moreover, the other undesirable side effects, in this proposed algorithm in the worst case are equal to that of the base algorithms.
F.1. General
A. Telikani; A. Shahbahrami; R. Tavoli
Abstract
Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that ...
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Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved against association rule mining method. This process strongly rely on the minimizing the impact of data sanitization on the data utility by minimizing the number of lost patterns in the form of non-sensitive patterns which are not mined from sanitized database. This study proposes a data sanitization algorithm to hide sensitive patterns in the form of frequent itemsets from the database while controls the impact of sanitization on the data utility using estimation of impact factor of each modification on non-sensitive itemsets. The proposed algorithm has been compared with Sliding Window size Algorithm (SWA) and Max-Min1 in term of execution time, data utility and data accuracy. The data accuracy is defined as the ratio of deleted items to the total support values of sensitive itemsets in the source dataset. Experimental results demonstrate that proposed algorithm outperforms SWA and Max-Min1 in terms of maximizing the data utility and data accuracy and it provides better execution time over SWA and Max-Min1 in high scalability for sensitive itemsets and transactions.
