A Secure Based Preserving Social Media Data Mangement System

Abstract

Personalized suggestions are important to help users find relevant information. It often depends on huge collection of user data, especially users’ online activity (e.g., liking/commenting/sharing) on social media, thereto user interests. Publishing such user activity makes inference attacks easy on the users, as private data (e.g., contact details) are often easily gathered from the users’ activity data. during this module, we proposed PrivacyRank, an adjustable and always protecting privacy on social media data publishing framework , which protects users against frequent attacks while giving personal ranking based recommendations. Its main idea is to continuously blur user activity data like user-specified private data is minimized under a given data budget, which matches round the ranking loss suffer from the knowledge blurring process so on preserve the usage of the info for enabling suggestions. a true world evaluation on both synthetic and real-world datasets displays that our model can provide effective and continuous protection against to the info given by the user, while still conserving the usage of the blurred data for private ranking based suggestion. Compared to other approaches, Privacy Rank achieves both better privacy protection and a far better usage altogether the rank based suggestions use cases we tested.