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An efficient and secure recommendation system based on federated matrix factorization in digital economy

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Abstract

The recommendation system is widely used in the digital economy to establish relationship between users and products. However, the data is usually scattered in different organizations in the form of “data islands” to protect privacy. To solve the above problem, we proposed an efficient and secure recommendation system based on federated matrix factorization, in which every user trains the model locally with their own data and uploads the calculated gradient vector instead of the original data. In the scheme, one server verifies the aggregation results transmitted by other servers and completes the model update without revealing user privacy. Additionally, users are allowed to drop out during the training process, and each participant’s behavior will be recorded to calculate the rewards or punishments. Finally, we evaluated the performance from different perspectives and proved its feasibility.

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Funding

This work was supported in part by the National Key Research and Development Project under Grant 2018YFC0831800; in part by the National Natural Science Foundation of China under grants 62072065 and 61932006; in part by Key Project of Technology Innovation and Application Development of Chongqing (cstc2019jscx-mbdxX0044, cstc2019jscx-mbdxX0064, cstc2019jscx-mbdxX0020) and Overseas Returnees Innovation and Entrepreneurship Support Program of Chongqing(cx201 8015,cx2020004).

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  1. School of Big Data & Software Engineering, Chongqing University, Chongqing, 400044, China

    Hongyu Chen, Chunlei Fu & Chunqiang Hu

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  1. Hongyu Chen
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  2. Chunlei Fu
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  3. Chunqiang Hu
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Correspondence to Chunlei Fu or Chunqiang Hu.

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Chen, H., Fu, C. & Hu, C. An efficient and secure recommendation system based on federated matrix factorization in digital economy. Pers Ubiquit Comput 27, 1595–1606 (2023). https://doi.org/10.1007/s00779-021-01646-w

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