Details

Autor(en) / Beteiligte

• Gunawan, Dedi
• Nugroho, Yusuf Sulistyo
• -, Maryam

Titel

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

Ist Teil von

• International journal of advanced computer science & applications, 2021, Vol.12 (11)

Ort / Verlag

West Yorkshire: Science and Information (SAI) Organization Limited

Erscheinungsjahr

2021

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• Sharing a transaction database with other parties for exploring valuable information becomes more recognized by business institutions, i.e., retails and supermarkets. It offers various benefits for the institutions, such as finding customer shopping behavior and frequently bought items, known as fre-quent itemsets. Due to the importance of such information, some institutions may consider certain frequent itemsets as sensitive information that should be kept private. Therefore, prior to handling a database, the institutions should consider privacy preserving data mining (PPDM) techniques for preventing sen-sitive information breaches. Presently, several PPDM methods, such as item suppression-based methods and item insertion-based methods have been developed. Unfortunately, the methods result in significant changes to the database and induce some side effects such as hiding failure, significant data dissimilarity, misses cost, and artificial frequent itemset occurrence. In this paper, we propose a swapping-based data sanitization method that can hide the sensitive frequent itemset while at the same time it can minimize the side effects of the data sanitization process. Experimental results indicate that the proposed method outperforms existing methods in terms of minimizing the side effects.