The pattern classification process involves the learning from the original training samples, which easily leads to privacy disclosure. In order to avoid the leaks of privacy in the pattern classification process and not to affect the performance of the algorithm, this paper proposes a pattern classification privacy preserve algorithm based on the primary component analysis (PCA). This algorithm extracts the principal component of the original training data and converts the original training samples to new samples corresponding to the primary components. Then, a classification model is trained on the new samples. Experiments are carried out on the Adult data set and the KDD CUP 99 data set, and the precision and recall indexes are used to evaluate the proposed algorithm. It is shown that this algorithm can avoid the leakage of the original attributes through extracting the principal components of the feature attributes about the raw data. PCA can achieve de-noising to some extent, so that the classification performance on the classifier is better than that on the original data set. Therefore, compared with the existing algorithms, this algorithm has better pattern classification accuracy and privacy preserve performance.
YUAN Yongbin
,
YANG Jing
,
ZHANG Jianpei
,
YU Xu
. A Pattern Classification Privacy Preserve Algorithm for Sparse Data Based on Primary Component Analysis[J]. Science & Technology Review, 2014
, 32(12)
: 68
-73
.
DOI: 10.3981/j.issn.1000-7857.2014.12.010
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