Random Rotation Based Data Perturbation Technique for Privacy Preserving Data Mining

Privacy-preserving data mining is study of valid mining models and patterns which mask private information and thus preserves privacy of the data. Many privacy preserving data mining techniques have been studied. Moreover existing techniques for privacy-preserving data mining are designed for traditional static databases and are not suitable for data streams. Recently, data streams are emerging as a new type of data, which are different from traditional static data. The features of data streams are: Data has timing preference; data distribution changes constantly with time; the amount of data is enormous; data flows in and out with fast speed; and immediate response is required. If the data changes, it would be necessary to rescan the database, which leads to more computation time and inability to promptly respond to the user. Moreover, it is observed that accuracy of data decreases when transformation on data is carried out. So there is need to develop system which preserves privacy along with accuracy. So the privacy preservation issue of data streams mining is very important issue.