Visible to the public An Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy

TitleAn Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy
Publication TypeConference Paper
Year of Publication2020
AuthorsWang, J., Wang, A.
Conference Name2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS)
Keywordscentralized recommendation, collaborative filtering, collaborative filtering recommendation model, composability, data privacy, Differential privacy, differential privacy constraint, differential privacy matrix factorization model, differential privacy protection, gradient methods, Human Behavior, Matrix decomposition, matrix factorization, MovieLens, Netflix, noise matrix factorization model, Perturbation methods, potential characteristic matrix, Prediction algorithms, Predictive models, privacy, privacy protection, pubcrawl, recommendation accuracy loss, recommendation systems, recommender systems, Resiliency, Scalability, software engineering, stochastic gradient descent algorithm, Stochastic processes, ε-differential privacy
AbstractIn this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.
Citation Keywang_improved_2020