Differential privacy data publishing method based on cell merging

With the emergence and rapid development of the application requirements of data publishing and data mining, how to protect the privacy data and prevent sensitive information leakage has become a great challenge. As a new privacy protection framework, differential privacy can provide privacy protection to the data. But the uniform grid method based on differential privacy has not considered the density and the sparsity of the data distribution, query deviation is too large. Therefore, this paper proposes a differential privacy data publishing method based on cell merging. To solve the problem of sparse data density and better balance noise deviation and uniform assumptions deviation, the paper gives the corresponding data partition algorithm, data merging algorithm. The accuracy and efficiency of the algorithm are compared with the uniform grid method and the adaptive grids approach algorithms, and the results show that it can keep the data validity and reduce the deviation of the query, at the same time,it has the higher accuracy and efficiency.