Preserving Privacy Using Data Perturbation in Data Stream

Data stream can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process. Examples of data streams include computer network traffic, phone conversations, web searches and sensor data etc. The data owners or publishers may not be willing to exactly reveal the true values of their data due to various reasons, most notably privacy considerations. To preserve data privacy during data mining, the issue of privacy preserving data mining has been widely studied and many techniques have been proposed. However, existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. So the privacy preservation issue of data streams mining is need for the time. This paper focused on describing a method that extends the process of data perturbation on data sets to achieve privacy preservation. The technique mainly exploits a combination of isometric transformations i.e. translation and rotation transformations used with a secure random function in order to provide secrecy of user-specified attributes without losing accuracy in results.