A Preserving Personal Privacy in Personalized Recommendation by protecting the Sensitive Subjects

  • Parmeshwari Varpe

Abstract

Recommender frameworks turn out to be progressively famous and broadly connected these days. The release of users’ personalized information is required to give clients exact proposals, yet this may put users at risk. Due to the troubles of personal privacy, user’s willingness to expose this data has turned into a noteworthy obstacle for improvement of personalized Recommendation. So the motive is to safeguard the sensitive subject. In this work, it is proposed to create a gathering of dummy preference view, to protect user’s sensitive subjects. Firstly, a client based structure for user security assurance is introduced, which does not need any modification to existing algorithms, as well no trade off to the proposal exactness. Then a privacy protection model formulated by the prime requirements such as similarity in the feature diffusion and the degree of exposure is put forth. Feature distribution measures the success of counterfeit preference profile to envelop unique user profile and the degree of exposure measures the favorable result of counterfeit preferences to envelop sensitive subject. Then finally based upon the subject archive of item characterization, algorithm to meet expected level of protection is introduced.

Keywords: Personalized Recommendation, Personal Privacy, Sensitive Subject.

References

[1] Zibin Zheng, Hao Ma, M. R. Lyu et al. ―Qos-aware web service recommendation by collaborative filtering‖. IEEE Transactions on Services Computing, 2011, 4 (2): 140–152. [2] F. Cacheda, V. Carneiro, D. Fernndez et al. ―Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender Systems‖. ACM Transactions on the Web, 2011 5 (1): Article 2 [3] Silvia Puglisi , Javier Parra-Arnau , Jordi Forn et al. ―On content based recommendation and user privacy in social-tagging systems‖. Computer Standards & Interfaces, 2015, 41: 17–27 [4] Khalid O, Khan M U S, Khan S U et al. ―OmniSuggest: A ubiquitous cloud-based context-aware recommendation system for mobile social networks‖. IEEE Transactions on Services Computing, 2014, 7 (3): 401–414.
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How to Cite
Varpe, P. (2018). A Preserving Personal Privacy in Personalized Recommendation by protecting the Sensitive Subjects. Asian Journal For Convergence In Technology (Founded by ISB &M School of Technology )), 4(I). https://doi.org/10.33130/asian journals.v4iI.419
Section
Computer Science and Engineering