Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process.