Abstract
In this research, we present a way to improve a secure multi-party computation-based recommender system solution. This solution is both efficient and protective of users’ personal information. Both theoretical and empirical research demonstrate that the proposed technique protects the participants’ personal information while maintaining the recommender system’s accuracy. The proposed solution is also more cost-effective in terms of both communication and computing than the original ones.
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Vu-Thi, V., Luong-The, D. (2023). An Elliptic Curve-Based Privacy-Preserving Recommender System. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_28
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DOI: https://doi.org/10.1007/978-3-031-46781-3_28
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