Privacy Preserving Similarity Measurement

Автор: ZHANG Guo-rong

Журнал: International Journal of Wireless and Microwave Technologies(IJWMT) @ijwmt

Статья в выпуске: 4 Vol.1, 2011 года.

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Data similarity measurement is an important direction for data mining research. This paper is concentrated on the issue of protecting the underlying attribute values when sharing data for the similarity of objects measurement and proposes a simple data transformation method: Isometric-Based Transformation (IBT). IBT selects the attribute pairs and then distorts them with Isometric Transformation. In the process of transformation, the goal is to find the proper angle ranges to satisfy the least privacy preserving requirement and then randomly choose one angle in this interval. The experiment demonstrates that the method can distort attribute values, preserve privacy information and guarantee valid similarity measurement.

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Privacy Preserving, Similarity, Isometric Transformation

Короткий адрес: https://sciup.org/15012750

IDR: 15012750

Список литературы Privacy Preserving Similarity Measurement

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