Improved Denclue Outlier Detection Algorithm With Differential Privacy and Attribute Fuzzy Priority Relation Ordering

Outlier detection is an important method in data mining. Although Denclue algorithm is particularly good at finding clusters of arbitrary shape and detecting outliers, it does not protect the user’s privacy well in the operation process. In this paper, differential privacy technology is introduced into Denclue algorithm to ensure the privacy security in the application of Denclue algorithm and outlier detection. Firstly, the differential privacy technology is used to add the Laplacian noise to the density to realize the sensitive information hiding among the data objects. Secondly, in order to compensate for the decrease of outlier detection accuracy caused by noise, the information entropy weight distance was introduced to amplify the influence of important attributes in the algorithm, and the density function of entropy weight distance was used to calculate each data point. Finally, through the method of ordering fuzzy priority relation, a new measure index is defined by analogy to measure the degree of outliers among the attributes. According to the measure index, the attributes are reordered and the weight distance of information entropy is improved. A differential privacy the Denclue outlier detection algorithm based on attribute fuzzy priority ordering (EAF-DP-Denclue) is proposed. The numerical results of the experiment show that the performance of EAF-DP-Denclue is more than that of traditional algorithms, and the identification process of EAF-DP-Denclue protects sensitive privacy information, and is better than that of the DP-DBScan outlier detection algorithm.