Generalization step analysis for privacy preserving data publishing

Data publishing generate much attention over the protection of individual privacy. In this paper, we show that it is necessary to reduce the steps of generalization in order to minimize information loss in privacy preserving data publishing, but sometimes the anonymous table on basis of the method could still be attacked when an attacker can possibly determine the privacy requirement and anonymous operations by examining the published data, or its documentation, and learn the mechanism of the anonymous algorithm. We call such an attack an advanced attack. To solve the problem, the condition of attack is analyzed, and a m-threshold model is presented to decide whether the value of quasi-identifier attribute would be continuously generalized, making use of algorithm of the GSSK(Generalization Step Safe of K-anonymity) to deal with the model. Finally, computer experiments show that the GSSK algorithm can prevent the attack with little information loss.