A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm
This addresses privacy concerns for users in recommender systems, but it is incremental as it builds on existing probabilistic methods.
The paper tackles the problem of privacy risks in neighborhood-based collaborative filtering, specifically the kNN attack, by proposing a Partitioned Probabilistic Neighbour Selection method that ensures required security while achieving optimal prediction accuracy, with theoretical and experimental validation.
The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering have attracted noticeable public concern recently. Specifically, $k$NN attack discloses the target user's sensitive information by creating $k$ fake nearest neighbours by non-sensitive information. Among the current solutions against $k$NN attack, the probabilistic methods showed a powerful privacy preserving effect. However, the existing probabilistic methods neither guarantee enough prediction accuracy due to the global randomness, nor provide assured security enforcement against $k$NN attack. To overcome the problems of current probabilistic methods, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required security guarantee while achieving the optimal prediction accuracy against $k$NN attack. In this paper, we define the sum of $k$ neighbours' similarity as the accuracy metric $α$, the number of user partitions, across which we select the $k$ neighbours, as the security metric $β$. Differing from the present methods that globally selected neighbours, our method selects neighbours from each group with exponential differential privacy to decrease the magnitude of noise. Theoretical and experimental analysis show that to achieve the same security guarantee against $k$NN attack, our approach ensures the optimal prediction accuracy.