Data stream is a continuous and changing sequence of data that continuously arrive at a system to store or process. It is vital to find out useful information from large enormous amount of data streams generated from different applications viz. organization record, call center record, sensor data, network traffic, web searches etc. Privacy preserving data mining techniques allow generation of data for mining and preserve the private information of the individuals. In this paper, classification algorithms were applied on original data set as well as privacy preserved data set. Results were compared to evaluate the performance of various classification algorithms on the data streams that had been privacy preserved using anonymization techniques. The paper proposes an effective approach for classification of anonymized data streams. Intensive experiments were performed using appropriate data mining and anonymization tools. Experimental result shows that the proposed approach improves accuracy of classification and increases the utility, i.e. accuracy of classification while minimizing the mean absolute error. The proposed work presents the anonymization technique effective in terms of information loss and the classifiers efficient in terms of response time anddata usability.
Aradhana Nyati : Research Scholar, Department of Computer Science and Engineering at Sir Padampat Singhania University, Udaipur, India has completed her post-graduation and graduation form MLS University Udaipur, Rajasthan in specialization with Information Technology. She has exposure to the academics and pursuing research in the field of data mining. Her area of research interest is data mining, network security, query processing and optimizing and dynamic data management.
Divya Bhatnagar : is working as Professor in the department of Computer Science and Engineering in School of Engineering, Sir Padampat Singhania University, Udaipur, India. She holds 18 years of teaching and research experience. Her specialization areas include data mining and Neural Networks.
Data mining, Privacy Preservation, Data Stream, Privacy preservation data mining, Anonymization, Classification, ARX-Tool
Applying the proposed framework, it was observed that amongst all anonymization techniques, minimum information loss was observed in population uniqueness. It was also observed that J-Rip when applied on the anonymization with k-anonymity and population uniqueness, J-48 applied on k-anonymity, Naive Bayes applied on k-Anonymity and population uniqueness produced good results. On the basis of these observationsit is concluded that J-48 when applied on anonymization with population uniqueness and k-anonymity generated best results.Classification of anonymized data stream indicated increase in utility and decrease in mean absolute error.
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