Shuffled Differential Privacy Method based on k-modes Clustering Algorithm
This paper proposes for the first time a shuffling differential privacy protection scheme(SDPk-modes)based on k-modes clustering algorithm.SDPk-modes are divided into different groups according to the distance between each data to obtain enough fine-grained optimization effect.The gradient stochastic perturbation technology is used to calculate the optimal probability less time.In the process of k-modes clustering,the feature vector that frequently appears in the data is taken as the cluster center point,and the distance measurement method based on attribute entropy speeds up the algorithm convergence to the cluster center,solves the problems of slow clustering speed and easy to fall into local optimality of the original algorithm,and significantly improves the clustering effect.Experimental verification shows that the proposed scheme is superior to the current similar schemes.
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