Privacy leakage and utility measurement are widely concerned issues in multi-attribute datasets by non-interactive differential privacy publishing. In this paper

we have proposed several quantification methods by using information entropy and hamming distortion to quantify the privacy of published dataset

utility of dataset and risk of privacy leakage. First

we have tailored the existing mutual information concept to analyze the relationship among associated attributes and constructed an associated dependency graph model to analyze their correlations among multi-attribute. After that

we have developed a privacy quantification method based on information entropy and privacy leakage Markov chain

which is generated based on the graph of privacy leakage path that has a valid efficiency measurement of the privacy leakage leading by associated attributes. Finally

to justify the efficiency of the proposed model

we have included an illustrative example and demonstrated the advantage of our method by comparing with other methods.