GSSK: A Generalization Step Safe Algorithm in Anonymizing Data
Pin Lv, YU Wen-bing, Niansheng Chen · 2010
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. 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 SSGK dealing with the model. Computer experiments show that the GSSK algorithm can prevent the attack with little information loss.