Accuracy-Privacy Comparison for Enhanced Grouping using a Hybrid Data Mining Technique

Privacy preserving data mining techniques play very important role in protecting the sensitive and private information of users from distributed data sets. In this modern field of business world applications the capability of storage and record the personal data or information of customers or of any user increase day by day due to this users are worried about the misuse and about the protection of their sensitive information in many data mining techniques that why privacy preserving data mining plays very important role for security and also protect the private data to a greater privacy and proper utilization by making a balance point between privacy and accuracy. In this paper, we are proposing a technique for extended grouping of data sets which provide privacy preserving data mining using ID3 algorithm with k-mean clustering algorithm on randomization response technique. It concludes that the accuracy of the extended group can be increase if we can classify the bounding limit of the data set using Genetic algorithm supported by k- mean. The proposed work is to check out the accuracy level of the dataset if the clusters are divided into inner clusters so that their privacy can be increased.