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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Adopting Non-linear Programming to Select Optimum Privacy Parameters for Multi-parameters Perturbation Algorithm for Data Privacy Improvement in Recommender Systems
verfasst von : Reham Kamal, Wedad Hussein, Rasha Ismail
Erschienen in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018
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Abstract
Recommendation system has witnessed a significant improvement with the introduction of data mining. Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. In order to preserve the privacy of the client in data mining process, the issue of information protection has become more urgently demanded. In this paper, an innovative system for movies recommendation is proposed. The new proposed system is fundamentally based on modified version of multi-parameters perturbation and query restriction as well as adopting non-linear programming strategy to select optimum privacy parameters. The results showed that the proposed framework is capable of providing the maximum security for the information available without decreasing the accuracy of recommendation.