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Improved collaborative filtering recommendation algorithm based on differential privacy protection

Yin, Chunyong ; Shi, Lingfeng ; Sun, Ruxia ; Wang, Jin

The Journal of supercomputing, 2020-07, Vol.76 (7), p.5161-5174 [Periódico revisado por pares]

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Título:
Improved collaborative filtering recommendation algorithm based on differential privacy protection
Autor: Yin, Chunyong ; Shi, Lingfeng ; Sun, Ruxia ; Wang, Jin
Assuntos: Algorithms ; Collaboration ; Compilers ; Computer Science ; Filtration ; Interpreters ; Privacy ; Processor Architectures ; Programming Languages
É parte de: The Journal of supercomputing, 2020-07, Vol.76 (7), p.5161-5174
Descrição: In order to receive efficient personalized recommendation, users have to provide personal information to service providers. However, in this process, personal private data are in an extremely dangerous situation. Personalized recommendation technology based on privacy protection can enable users to enjoy personalized recommendations, while private data are also protected. In this paper, an efficient privacy-preserving collaborative filtering algorithm is proposed, which is based on differential privacy protection and time factor. The proposed method used the MovieLens data set in the experiment. Experimental results showed that the proposed method can effectively protect the private data, but the accuracy of recommendation is slightly inferior than the traditional collaborative filtering algorithm.
Editor: New York: Springer US
Idioma: Inglês

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