Reconstructing rated items from perturbed data

Abstract

The basic idea behind privacy-preserving collaborative filtering schemes is to prevent data collectors from deriving the actual rating values and the rated items. Different data perturbation methods have been proposed to protect individual privacy. Due to different privacy concerns, users might disguise their data variably to meet their own privacy concerns. In addition to reconstructing the true rating values, data collectors might try to reconstruct the rated items. In this paper, our goal is to reconstruct the rated items with the help of auxiliary information when users mask their confidential data inconsistently in privacy-preserving prediction systems. We first need to estimate the number of the rated items. Then we have to predict the rated items. To do so, we first use existing methods to eliminate noise from the disguised data. We improve our predictions by utilizing the auxiliary information. Our real data-based empirical outcomes show that our proposed approaches are able to reconstruct the rated items with decent accuracy in spite of variable data masking