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Privacy preserving is an important issue in data publishing. Many anonymization algorithms are available in meeting the privacy requirements of the privacy models such as k-anonymity, l-diversity and t-closeness. In this paper, we discuss the requirements that anonymized data should meet and propose a new data anonymization approach based on tradeoff between utility and privacy to resist probabilistic inference attacks. To evaluate the quality of anonymized results, a method of measuring the utility loss and privacy gain of anonymized data is brought out which can be used to find the optimal anonymization solution. The result of the experiments validates the availability of the approach.