CAJ | 학술논문

针对大规模数据集上的模式分类任务，提出了一种基于 Parzen 窗核密度估计的模式分类隐私保护算法。该算法首先利用 Parzen 窗算法对原始大规模训练集服从的概率密度进行估计，然后根据估计的概率密度函数构造 la 个替换训练样本，其中 l 为原始样本的数目，a 通过10折交叉验证方式确定。最后发布替换训练样本进行模式分类，以实现原始数据上的隐私保护。在 Adult 数据集上的仿真实验充分验证了该算法的有效性。
침대대규모수거집상적모식분류임무，제출료일충기우 Parzen 창핵밀도고계적모식분류은사보호산법。해산법수선이용 Parzen 창산법대원시대규모훈련집복종적개솔밀도진행고계，연후근거고계적개솔밀도함수구조 la 개체환훈련양본，기중 l 위원시양본적수목，a 통과10절교차험증방식학정。최후발포체환훈련양본진행모식분류，이실현원시수거상적은사보호。재 Adult 수거집상적방진실험충분험증료해산법적유효성。
In this paper, we proposed a pattern classification privacy preserve algorithm based on Parzen window kernel density estimation on large scale dataset. Firstly, the probability density followed by the original large scale training set is estimated. Then we can construct replacement training samples by the estimated probability. Finally, the replacement training samples are published for 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.