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- W2733829042 abstract "Abstract Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. This paper proposes a perturbation and anonymization technique that is performed on the vertically partitioned data. A third-party coordinator is used to partition the data recursively in various parties. The parties perturb the data by finding the mean, when the specified threshold level is reached. The perturbation maintains the statistical relationship among attributes." @default.
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- W2733829042 date "2017-06-01" @default.
- W2733829042 modified "2023-10-14" @default.
- W2733829042 title "Partition Based Perturbation for Privacy Preserving Distributed Data Mining" @default.
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- W2733829042 doi "https://doi.org/10.1515/cait-2017-0015" @default.
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