Effective sanitization approaches to hide sensitive utility and frequent itemsets
Privacy preserving data mining is a vibrant area in data mining. The sharing of data between the organizations is found to be beneficial for business growth. However, privacy policies and threats prevent the data owners from sharing the data for mining. The current data sanitization approaches focus on hiding either frequent itemsets or utility itemsets separately. This paper proposes to study the problem of hiding the sensitive utility and frequent itemsets. To resolve this problem, two effective data sanitization algorithms MSMU and MCRSU are presented to hide the sensitive utility and frequent itemsets in the modified database. While hiding the sensitive itemsets, the algorithms sanitize the database with minimum impact on the non-sensitive itemsets. To accomplish this, MSMU is devised to identify the victim items with minimum support and maximum utility whereas MCRSU uses conflict ratio. Results from the computational experiments on the synthetic and real datasets indicate that MCRSU algorithm is more effective than MSMU in minimizing the non-sensitive itemsets affected as well as maintaining data quality in the sanitized database.
sanitization utility and frequent itemset privacy preserving data mining utility mining