Privacy preserving recommender system based on improved MASK and query restriction

In the last few decades, recommendation systems have received an iconic representation in the field of information technology. With the noticed rapid advancement of data mining, the issue of privacy has become an inevitable necessity. Hence, the main challenge that accompanies data mining is developing a cutting-edge strategy to protect private information. In this paper, we suggest a framework of recommendation for privacy protection based on an improved version of mining association with secrecy Konstraints'(MASK) using data perturbation and query restriction. Experimental results showed that our proposed system performance is high and can protect data privacy without decreasing the recommendations accuracy.