An Outlier Detection Algorithm Based on Differential Privacy

Aiming at the problem that personal privacy is vulnerable to damage during outlier detection, this paper proposes an outlier detection method based on differential privacy. The algorithm uses the minimum spanning tree path to characterize the dissimilarity of the data, adds Laplace noise to the weight of the edge of the minimum spanning tree(MST),effectively resists background knowledge attacks. At the same time, combining the degree of dissimilarity and reverse k-similar number, a new anomaly judgment method is proposed, it improves the outlier detection rate. The experimental analysis shows that the algorithm can effectively protect the sensitive attributes of the data, improve the true positive rate(TPR) of outlier detection and reduce the false positive rate(FPR).