Principal Component Analysis Based Transformation for Privacy Preserving in Data Stream Mining
- Prachi Patel
- Ompriya Kale
- Bhavin Thakkar
Data Stream, Data Perturbation, Data Perturbation, Random Function
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 techniques for Principal Component Analysis (PCA) based transformation for stream data using Massive Online Analysis (MOA). The clustering accuracy while using the transformed data is almost equal to the original dataset.
Prachi Patel, Ompriya Kale, Bhavin Thakkar. "Principal Component Analysis Based Transformation for Privacy Preserving in Data Stream Mining".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.2, Issue 2, pp.2384-2391, URL :https://rjwave.org/ijedr/papers/IJEDR1402175.pdf
Volume 2 Issue 2
Pages. 2384-2391
