RANK-BASED WEIGHTED ASSOCIATION RULE MINING TECHNIQUE FOR SECURE CLOUD COMPUTING ENVIRONMENT

Currently, the online services are used for all business activities. This produces enormous amount of data every day and all these data is to be stored in different cloud data storages. To identify the best and useful information out of these data and to take a wise decision from the available information is considered as one of the challenging issues. To address this major issue, few researchers adopted data mining techniques such as clustering, classification and association models for mining the useful information from the cloud environment. Although Cloud Computing is a powerful means of achieving high storage and computing services at a low cost, it is revealed that due to challenging security issues, many cloud users are not showing interest to use cloud based services. This research work proposes an efficient Secure Cloud Data Mining Model through Apriori-HUDS Rule Miner to address these issues. This secure cloud data mining model is designed in such a way that it facilitates the cloud users to identify the best frequently used data sets from the cloud data centers for their queries and also provides high security to the cloud servers. Thus, the service providers can protect the sensitive data of clients. The proposed model is implemented in the cloud environment and thoroughly studied. From our experimental results, it is noticed that the proposed model performs well in terms of predicting frequently accessed data sets by cloud users, ranking data sets with highest support and achieving high memory utilization as compared with existing cloud data mining model