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Evaluating the Privacy of User Profiles in Personalized Information Systems

Yobu Uppalapati*, lalitha.B**

* M. Tech Scholar (Artificial Intelligence), CSE Department, JNTUA College of Engineering, Anantapuramu, A.P, India.

** Assistant Professor, CSE Department, JNTUA College of Engineering, Anantapuramu, A.P, India.

Periodicity:April - June'2015
DOI : https://doi.org/10.26634/jse.9.4.3532

Abstract

Collaborative tagging is one of the most well-known and widespread services available online. The key point of collaborative tagging is to distinguish the resources based on user opinion, stated in the form of tags. Collaborative tagging supplies the source for the semantic Web, network will connect all online resources based on their meanings. While this information is a valued source, its total volume limits its value. Most of the research projects and corporations are discovering the use of personalized applications that control this overflow by modifying the information obtainable to individual users. These applications altogether utilize some information about individuals in directive to be active. This zone is generally called user profiling. In this paper some of the most standard techniques for gathering information about users, signifying, and constructing user profiles. This paper mainly focus on measuring the privacy of user profiles through kl divergence and Shannon entropy techniques showing the tag suppression that protects the end user privacy.

Keywords

Policy-based Collaborative Tagging, Tag Annihilation, Privacy-enhancing Technology, Social Networking.

How to Cite this Article?

Yobu, U., and Lalitha, B. (2015). Evaluating the Privacy of User Profiles in Personalized Information Systems. i-manager’s Journal on Software Engineering, 9(4), 20-24. https://doi.org/10.26634/jse.9.4.3532

References

[1]. B. Carminati, E. Ferrari, and A. Perego, (2009). “Combining social networks and Semantic Web technologies for personalizing Web access," in Collaborative Computing: Networking, Applications and Worksharing, ser. LNICST. Springer, Vol.10, pp.126-144.

[2]. S. P. Lloyd, (1982). “Least squares quantization in PCM," IEEE Trans. Inform. Theory, Vol.IT-28, pp.129-137.

[3]. E. Michlmayr and S. Cazer, (2007). “Learning user profiles from tagging data and leveraging them for personal(ized) information access," in Proc. Workshop Tagging and Metadata for Social Inform. Org. Workshop in Int. WWW Conf.

[4]. B. Markines, C. Cattuto, F. Menczer, D. Benz, A. Hotho, and G. Stum, (2009). “Evaluating similarity measures for emergent semantics of social tagging," in Proc. Int. WWW Conf. ACM, pp. 641-650.

[5]. Z. Yun and F. Boqin, (2008). “Tag-Based User Modeling Using Formal Concept Analysis,” Proc. IEEE Eighth Int'l Conf. Computer Information Technology (CIT), pp.485- 490.

[6]. A. Shepitsen, J. Gemmell, B. Mobasher, and R. Burke, (2008). “Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering,” Proc. ACM Conf. Recommender Systems (RecSys), pp.259-266.

[7]. M. Bundschus, S. Yu, V. Tresp, A. Rettinger, M. Dejori, and H.-P. Kriegel, (2005). “Hierarchical Bayesian Models for Collaborative Tagging Systems,” Proc. IEEE Int'l Conf. Data Mining (ICDM), pp. 728-733.

[8]. H. Polat and W. Du, (2005). “SVD-Based Collaborative Filtering with Privacy,” Proc. ACM Int'l Symp. Applied Computing (SASC), pp. 791-795.

[9]. C. Marlow, M. Naaman, D. Boyd, and M. Davis, (2006). “HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, to Read,” Proc. 17th Conf. Hypertext and Hypermedia (HYPERTEXT), pp. 31-40.

[10]. Berners-Lee, R. Fielding, and L. Masinter, “Uniform Resource Identifier (URI): Generic Syntax," RFC 3986 (Internet Standard), Internet Engineering Task Force, Jan. 2005, updated by RFC 6874. [Online]. Available: http://www.ietf.org/rfc/rfc3986.txt

Evaluating the Privacy of User Profiles in Personalized Information Systems

Abstract

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References

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