In this paper, a pattern classification privacy preservation algorithm is proposed based on the Parzen window kernel density estimation on large scale dataset. Firstly, the probability density is estimated through the original large scale training set. Then the replacement training samples are constructed by the estimated probability. Finally, the replacement training samples are published for the pattern classification training. Thus the privacy on the original training set can be protected effectively. The simulation experiments on Adult datasets fully verify the effectiveness of the proposed algorithm.

Parzen window  /  kernel density estimation  /  data publish  /  privacy preserving