The invention discloses a differential privacy protection-oriented k-means clustering method. The K-means clustering method comprises the following steps: performing data preprocessing; ensuring thatC indicates a clustered centered point set, and C indicates a sum of error square of a given data set and a cluster center C; judging the volume of C; performing cyclic execution until retry is greater than a maximum value retrymatx of given retry times, and then returning to the best central point Cbest; traversing each point of the data set X, classifying the points to the nearest central point;setting added random noises; renewedly calculating the sum of the data points of each cluster and the quantity of the points, and adding the noises and finally updating the quality center of the cluster; and repeatedly carrying out the steps until the sum of error square is converged or iteration times reach the upper limit. According to the differential privacy protection-oriented k-means clustering method disclosed by the invention, the appropriate random noises which are specially distributed are added in an iteration process of a k-means clustering algorithm, so that a clustering result is distorted to a certain extent, the aim of privacy protection is fulfilled, and meanwhile, the availability of data is ensured.