Performance Evaluation of Anonymized Data Stream Classifiers

Nyati, Aradhana and Bhatnagar, Divya Performance Evaluation of Anonymized Data Stream Classifiers. International Journal of Computer Science and Network - IJCSN, 2016, vol. 5, n. 2. [Journal article (Unpaginated)]

[img] Text

Download (271kB)

English abstract

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.

Item type: Journal article (Unpaginated)
Commentary on: Eprints 0 not found.
Keywords: Data mining, Privacy Preservation, Data Stream, Privacy preservation data mining; Anonymization; Classification; ARX-Tool
Subjects: L. Information technology and library technology > LN. Data base management systems.
Depositing user: IJCSN Journal
Date deposited: 10 Aug 2016 06:21
Last modified: 10 Aug 2016 06:21

Commentary/Response Threads

  • Nyati, Aradhana and Bhatnagar, Divya Performance Evaluation of Anonymized Data Stream Classifiers. (deposited 10 Aug 2016 06:21) [Currently Displayed]


"SEEK" links will first look for possible matches inside E-LIS and query Google Scholar if no results are found.

[1] Benjamin C. M. Fung ,Wang K. , andPhilip S., “Anonymizing classification data for privacy preservation,” IEEE Trans on Knowledge And Data Engineering, vol.19, 2007, pp.711-725. [2] Aggarwal C. C. and Philip S.Yu. “A general survey of privacy-preserving data mining models and algorithms,” Springer, vol.12, 2008, pp.11-52. [3] Modi S. and Patel, “A. Privacy preserving data stream mining using two phase geometric data perturbation,” International J for Scientific Research & Development, vol.3, 2015, pp.1115-1118. [4] Chang J. H., and Lee W. S., “Finding recent frequent itemsets adaptively over online data stream,” Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C., U.S.A., 2003, pp. 487-492. [5] Cohen E. and Strauss M., “Maintaining time decaying stream aggregates,” Proceedings of the 22th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, San Diego, California, U.S.A., 2003, pp. 223-233. [6] Vachhani N. and Vaghela B., “Geometric data transformation for privacy preserving on data stream using classification,” IJIRCCE, vol.3, 2015, pp. 6013-6019. [7] Patil S., Thakkar N., and Firoj S., “Secure Communication Using Privacy Preserving in a Data Mining,” IJARCSSE, vol.5, 2015, pp.391-394. [8] Li Su, Hong-yan Liu and Zhen-Hui Song, “A new classification algorithm for data stream,” Intl J Modern Education and Computer Science, vol.4, 2011, pp. 32-39. [9] Ringne A.G., Sood D. and Toshniwal D. , “Compression and privacy preservation of data streams using moments,” Intl J of machine learning and computing, vol.1, 2011 pp.473-478. [10] Chhinkaniwala H., Patel K. and Garg S., “ Privacy preserving data stream classification using data perturbation techniques,” Intl Conf on Emerging Trends in Electrical, Electronics and Communication Technologies, 2012, pp. 1-8. [11] Pramod S. and Vyas O. P. , “Data stream mining: a review on windowing approach,” Global Journal of computer science and technology software and data engineering, vol.12, 2012, pp.27-30. [12] Trambadiya T.J. and Bhanodia P., “A heuristic approach to preserve privacy in stream data with classification,” Intl J of Engineering Research and Applications, vol.3, 2013, pp. 1096-1103. [13] Patel M., Richariya P. and Shrivastava A., “Privacy preserving using randomization and encryption methods,” Sch J Eng Tech, vol.1, 2013, pp.117-121. [14] Dhivakar and Mohana, “A Survey on privacy preservation recent approaches and techniques,” Intl J of Innovative Research in Computer and Communication Engineering, vol.2, 2014, pp.6559-6566. [15] Brijlal P. and Shah, “An Overview of privacy preserving techniques and data accuracy,” Intl J of Advance Research in Computer Science and Management Studies, vol.3, 2015 pp.135-140. [16] Yin, Y. et. al , “Privacy Preserving Data Mining” in Data Mining, Springer, 2011, pp. 101-115. [17] Devasena L., “Efficiency Comparison of Multilayer Perceptron and SMO Classifier for Credit Risk Prediction,” Intl J of Advanced Research in Computer and Communication Engineering, 2014, vol.3, pp. 6155-6162. [18] Patil M. and Ingale S., “Privacy Control Methods for Anonymous & Confidential Database Using Advance Encryption Standard,” Intl J of Computer Science and Mobile Computing, 2013, vol.2, pp. 224-229. [19] Lichman, M., “UCI Machine Learning Repository,” 2013. [20] [21]


Downloads per month over past year

Actions (login required)

View Item View Item