International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Privacy Ensured Contemplate With SVM and Role Based Classification with Collaborative Tagging

Author : A Divya ¹ S Madhu Sudhanan ² M Poornima ³

Date of Publication :7th April 2015

Abstract: Tagging system is one of the most diffused and popular services available online. This system allows users to add free text labels generally referred as tags to the Internet resources for example web pages, images, video, music and even blogs. Web metadata have a potential to improve search, retrieval and to protect end user from possible harmful content. The Organization updates their Company portal with public sharing data along with Sensitive data. The query is processed based on the User Profile Analysis. In actual system provide taxonomy of tagging system and system web technologies help to specify labels and rate for that labels which assess the trustworthiness of resources to enforce web access personalization. To enhance the efficiency of tag suppression the privacy ensured skim with Support Vector Machine along with Privacy Enhancing Technology is implemented. SVM is used for extraction of data and obscure delicate data. PET is achieved by using the technique Tag Suppression which has the role of providing the privacy for information. Web user will search using a keyword. The keyword may be the location, feedback or cost to analyze the data. The authentication of the portal is done by the management. Management classified as two roles they are Department Head Role and Admin. Department Head Role is to update their part of portal and retrieve only the corresponding data. Final authentication and approval is done by the admin. Through the analysis efficiency guarantees of proposed scheme is achieved.

Reference :

1. B. Markines, C. Cattuto, F. Menczer, D. Benz, A. Hotho, and S. Gerd, “Evaluating Similarity Measures for Emergent Semantics of social Tagging,” Proc. 18th Int’l Conf. World Wide Web(WWW),pp. 641 -650,2009.
2. C. Marlow,M. Naaman,D. Boyd, and M. Davis, “HT06, Tagging Paper, Taxonomy, Flickr, Academic Article to Read,”Proc.17thConf.HypertextandHypermedia(HYPERTE XT),pp. 31-40,2006.
3. B.Carminati, E. Ferrari, and A.Perego, “Combining Social Networks and Semantic Web Technologies For Personalizing Web Access, “Proc. Fourth Int’l Conf. Collaborative Computing:Networking,Applications and Worksharring,pp. 126-144,2008.
4. R. Gross and A. Acquisti, “Information Revelation and Privacy in Online Social Networks,” Proc. ACM Workshop Privacy Electronics Soc.(WPES),pp.71-80,2005
5. S. B. Barnes, “A Privacy Paradox:Social Networking in the United States,” First Monday, vol.11,no. 9,Sept.2006.
6. J. Parra-Arnau, D. Rebollo- Monedero, and J. Frone, “A Privacy Preserving Architecture for the Semantic Web Based on Tag Suppression,” Proc. Seventh Int’l Conf. Trust, Privacy, Security, Digital Business (TrustBus), pp. 58-68, Aug.2010
7. J. VoB, “ Tagging Folksonomy & Con – Renaissance of Manual Indexing?” Computer Reaserch Repository, vol.. abs/cs/0701072, 2007.
8. J. Castella `-Roca, A. Viejo, and J. Herrera-JoancomartÄ± ´, “Preserving User’s Privacy in Web Search Engines,” Computer Comm., vol. 32, nos. 13/14, pp. 1541-1551, 2009
9. D. Rebollo-Monedero, J. Forne ´, and J. DomingoFerrer, “Coprivate Query Profile Obfuscation by Means of Optimal Query Exchange between Users,” IEEE Trans. Dependable and Secure Computing, vol. 9, no. 5, pp. 641-654, Sept.-Oct. 2012.
10. C. Schmitz, A. Hotho, R. Jäschke, and G. Stumme. Mining association rules in folksonomies. In Data Science and Classification: Proc. of the 10th IFCS Conf., pages 261– 270, Berlin, Heidelberg, 2006. Springer.
11. P. Schmitz. Inducing ontology from Flickr tags. In WWW Collaborative Web Tagging Workshop, May 2006.
12. G. Mishne. Autotag: a collaborative approach to automated tag assignment for weblog posts. In Prox. 15th Intl. Conf. on World Wide Web, pages 953–954. ACM Press, 2006
13. J. Parra-Arnau, D. Rebollo-Monedero, and J. Forne ´, “A Privacy- Protecting Architecture for Collaborative Filtering via Forgery and Suppression of Ratings,” Proc. Int’l Workshop Data Privacy Manage- ment, Autonomous Spontaneus Security (DPM), pp. 42-57, Sept. 2011.
14. Z. Yun and F. Boqin, “Tag-Based User Modeling Using Formal Concept Analysis,” Proc. IEEE Eighth Int’l Conf. Computer Informa- tion Technology (CIT), pp. 485- 490, 2008
15. A. Shepitsen, J. Gemmell, B. Mobasher, and R. Burke, “Persona- lized Recommendation in Social Tagging Systems Using Hier- archical Clustering,” Proc. ACM Conf Recommender Systems (RecSys), pp. 259-266, 2008
16. K. Bischoff, C.S. Firan, W. Nejdl, and R. Paiu, “Can All Tags Be Used for Search?” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), pp. 193- 202, 2008.
17. X. Li, C.G.M. Snoek, and M. Worring, “Learning Social Tag Relevance by Neighbor Voting,” IEEE Trans. Multimedia, vol. 11, no. 7, pp. 1310-1322, Nov. 2009.
18. S. Marti and H. Garcia-Molina, “Taxonomy of Trust: Categorizing P2P Reputation Systems,” Computer Networks, vol. 50, pp. 472-484, Mar. 2006.
19. L. Specia and E. Motta, “Integrating Folksonomies with the Semantic Web,” Proc. Int’l Semantic Web Conf., pp. 624-639, 2007.
20. M. Bundschus, S. Yu, V. Tresp, A. Rettinger, M. Dejori, and H.-P. Kriegel, “Hierarchical Bayesian Models for Collaborative Tagging Systems,” Proc. IEEE Int’l Conf. Data Mining (ICDM), pp. 728-733, 2009.
21. H. Polat and W. Du, “SVD-Based Collaborative Filtering with Privacy,” Proc. ACM Int’l Symp. Applied Computing (SASC), pp. 791-795, 2005.