Privacy preserving social media data publishing for personalised ranking based recommendations
C Vijiyalakshmi, Prathana Elizabeth Jyoti J, R. Pavithra, R. Gethsi Sharmila · International journal of advance research and innovative ideas in education · 2019
In this paper, we proposed PrivRank, a customizable and continuous privacy-preserving social media data publishing framework protecting users against inference attacks while enabling personalized ranking-based recommendations. Its key thought is to ceaselessly jumble client action information with the end goal that the security spillage of client determined private information is limited under a given information bending spending plan, which limits the positioning misfortune brought about from the information jumbling process keeping in mind the end goal to save the utility of the information for empowering proposals.