A Web Application for Prevention of Inference Attacks using Crowd Sourcing in Social Networks

Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.