Introducing an algorithm for use to hide sensitive association rules through perturb technique

Abstract

Due to the rapid growth of the data mining technology, obtaining private data on users through this technology has become easier. Association rules mining is one of the data mining techniques that is used to extract useful patterns in the form of association rules. One of the main problems with the application of this technique to databases is the disclosure of sensitive data, and thus endangering the security and privacy of the owners of the data. Hiding the association rules is one of the methods available 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 have been presented so far. An algorithm for use 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 an attempt to remove the considered item from a transaction with the highest weight by allocating weight to the items and transactions. The efficiency of this technique is measured by means of the failure criteria of hiding, the number of lost rules and ghost rules, and the execution time. The results obtained from this work are assessed and compared with the two known FHSAR and RRLR algorithms, which are based on the two real databases dense and sparse. The results obtained indicated that the number of lost rules in all the experiments performed decreased by 47% in comparison with the RRLR algorithm, and decreased by 23% in comparison with the FHSAR algorithm. Moreover, the other undesirable side effects in the proposed algorithm in the worst case were equal to those for the basic algorithms.