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Reseach Article

Enhancing Privacy and Security in Personalized Web Search

by Priyanka A. Sonawane, Satpalsing D. Rajput
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 9
Year of Publication: 2016
Authors: Priyanka A. Sonawane, Satpalsing D. Rajput
10.5120/ijca2016911617

Priyanka A. Sonawane, Satpalsing D. Rajput . Enhancing Privacy and Security in Personalized Web Search. International Journal of Computer Applications. 150, 9 ( Sep 2016), 1-6. DOI=10.5120/ijca2016911617

@article{ 10.5120/ijca2016911617,
author = { Priyanka A. Sonawane, Satpalsing D. Rajput },
title = { Enhancing Privacy and Security in Personalized Web Search },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 9 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number9/26118-2016911617/ },
doi = { 10.5120/ijca2016911617 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:28.567224+05:30
%A Priyanka A. Sonawane
%A Satpalsing D. Rajput
%T Enhancing Privacy and Security in Personalized Web Search
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 9
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Personalized Web Search is a promising way to improve the accuracy of web search and has been attracting much attention recently. Data classification and prediction using searching are used for many purposes. Privacy and security of Personal information has the more challenging task in web mining. In existing system greedy algorithm is used and it generates decision tree which stores split pattern. If split pattern is disclose then complete tree data can be retrieved. So this can compromise privacy due to which it is unsecured. In proposed system train the system with dataset and calculate the probability of output classes. The probability calculation is personalized to the training dataset and output is secured by providing enhanced privacy. In Proposed Approach, System improve the relevancy and prediction of the information in order to get more accurate result for effective personalized web search. Experimental evaluation shows that, Results obtained by using proposed approach are more precise and relevant than existing approach.

References
  1. Y. Zhu, L. Xiong, and C. Verdery, “Anonymizing user profiles for personalized web search," ACM, April 2010.
  2. J. Castell-Roca, A. Viejo, and J. Herrera-Joancomart, “Preserving users privacy in web search engines," Elsevier, p. 1541- 1551, 2009.
  3. Y. Xu, K. Wang, G. Y. Ada, and W. C. Fu, “Online anonymity for personalized web services," ACM, November 2009.
  4. X. Shen, B. Tan, and C. Zhai, “Privacy protection in personalized search," ACM, pp. 4-17, June 2007.
  5. Z. Dou, R. Song, and J. Wen, “A large scale evaluation and analysis of personalized search strategies," ACM, pp. 581-590, May 2007.
  6. V. A. Kumari and R.Chitra, “Classification of diabetes disease using support vector machine," Journal of Computer Science, vol. 3, pp. 1797-1801, April 2013.
  7. S. Ghumbre, C. Patil, and A. Ghatol, “Heart disease diagnosis using support vector machine," ICCSIT, pp. 84-88, December 2011.
  8. S. Rani and M. S. Girdhar, “Analyse the heart disease and diabetes using artificial neural networks," IJARCSSE, vol. 4, pp. 1155-1157, August 2014.
  9. M. Shouman, T. Turner, and R. Stocker, “Using decision tree for diagnosing heart disease patients," Australasian Data Mining Conference, vol. 121, pp. 23-29, 2011.
  10. T. Jesmin and K. Ahmed, “Brain cancer risk prediction tool using data mining," ACM, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Personalized Web Search Privacy Security AES Encryption and Decryption.