Privacy Preserving Data Stream Classification Using Data Perturbation Techniques

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. These data sets need to be analyzed for identifying trends and patterns, which help us in isolating anomalies and predicting future behavior. However, 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. Hence, some amount of privacy preservation needs to be done on the data before it can be made publicly available. 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. Classification characteristics of original data streams and perturbed data streams using proposed algorithms have been evaluated in terms of less information loss, response time, and more privacy gain.