Privacy Preserving Naive Bayes Classification

Lecture notes in computer sciencePublished 1 January 2005

Peng Zhang, Yunhai Tong, Shiwei Tang, Dongqing Yang

DOI10.1007/11527503_88

Source URL

Citations35

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

This paper proposes a new randomization method, Randomized Response with Partial Hiding (RRPH), for distorting the original data, and an effective naive Bayes classifier is presented to predict the class labels for unknown samples according to the distorted data by RRPH.

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.

Keywords

Computer Science

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