Abstract
Personalized recommendation systems become increasingly popular and have been widely applied in various fields nowadays. The release of users’ private data is required in order to provide users recommendations with high accuracy, yet this has put the users in danger. Unfortunately, existing privacy preserving methods are either developed under trusted server settings with impractical private recommendation systems or lack of strong privacy guarantees.
In this paper, we propose a new scheme that can achieve a better balance between security and usability, effectively solving the above problem. The innovation lies in improving the method of adding noise to the Laplace mechanism in local differential privacy. It adopts a univariate noise increase method, combined with wavelet clustering and multiple cluster identification methods to ensure the lowest data distortion rate. At the same time, it is safe to ensure the data.
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Lv, Z. et al. (2020). Distributed Differential Privacy Protection System for Personalized Recommendation. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_15
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DOI: https://doi.org/10.1007/978-981-15-7530-3_15
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