SVD-based privacy preserving data updating in collaborative filtering

Collaborative Filtering technique is widely adopted by online service providers in their recommender systems. This technique provides recommendations based on users’ transaction history. To provide decent recommendations, many online merchants (data owner) ask a third party to help develop and maintain recommender systems instead of doing that themselves. Therefore, they need to share their data with these third parties and users’ private information is prone to leaking. Furthermore, with increasing transaction data, data owner should be able to handle data growth efficiently without sacrificing privacy. In this paper, we propose a privacy preserving data updating scheme for collaborative filtering purpose and study its performance on two different datasets. The experimental results show that the proposed scheme does not degrade recommendation accuracy and can preserve a satisfactory level of privacy while updating the data efficiently.