Visible to the public A Differential-Privacy-based hybrid collaborative recommendation method with factorization and regression

TitleA Differential-Privacy-based hybrid collaborative recommendation method with factorization and regression
Publication TypeConference Paper
Year of Publication2021
AuthorsYuan, Rui, Wang, Xinna, Xu, Jiangmin, Meng, Shunmei
Conference Name2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
KeywordsBig Data, Collaboration, Computational modeling, Differential privacy, Human Behavior, linear regression, matrix factorization (MF), movie preferences, Predictive models, privacy, pubcrawl, recommender system, recommender systems, resilience, Resiliency, Scalability
AbstractRecommender systems have been proved to be effective techniques to provide users with better experiences. However, when a recommender knows the user's preference characteristics or gets their sensitive information, then a series of privacy concerns are raised. A amount of solutions in the literature have been proposed to enhance privacy protection degree of recommender systems. Although the existing solutions have enhanced the protection, they led to a decrease in recommendation accuracy simultaneously. In this paper, we propose a security-aware hybrid recommendation method by combining the factorization and regression techniques. Specifically, the differential privacy mechanism is integrated into data pre-processing for data encryption. Firstly data are perturbed to satisfy differential privacy and transported to the recommender. Then the recommender calculates the aggregated data. However, applying differential privacy raises utility issues of low recommendation accuracy, meanwhile the use of a single model may cause overfitting. In order to tackle this challenge, we adopt a fusion prediction model by combining linear regression (LR) and matrix factorization (MF) for collaborative recommendation. With the MovieLens dataset, we evaluate the recommendation accuracy and regression of our recommender system and demonstrate that our system performs better than the existing recommender system under privacy requirement.
DOI10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00073
Citation Keyyuan_differential-privacy-based_2021