Abstract
Privacy preserving data mining is to discover accurate patterns without precise access to the original data. In this paper, we combine the two strategies of data transform and data hiding to propose a new randomization method, Randomized Response with Partial Hiding (RRPH), for distorting the original data. Then, an effective naive Bayes classifier is presented to predict the class labels for unknown samples according to the distorted data by RRPH. Shown in the analytical and experimental results, our method can obtain significant improvements in terms of privacy, accuracy, and applicability.
This work is supported by the National Natural Science Foundation of China under Grant No.60403041.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, P., Tong, Y., Tang, S., Yang, D. (2005). Privacy Preserving Naive Bayes Classification. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_88
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DOI: https://doi.org/10.1007/11527503_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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