Despite that the information discovered by data mining can be very valuable to many applications, people have shown increasingconcernabouttheothersideofthecoin,namelythe privacythreatsposedbydatamining[2].Individual’sprivacy may be violated due to the unauthorized access to personal data,theundesireddiscoveryofone’sembarrassinginformation, the use of personal data for purposes other than the one for which data has been collected, etc. For instance, the U.S. retailerTargetoncereceivedcomplaintsfromacustomerwho was angry that Target sent coupons for baby clothes to his teenagerdaughter.1 However,itwastruethatthedaughterwas pregnantatthattime,andTargetcorrectlyinferredthefactby mining its customer data. From this story, we can see that the conflict between data mining and privacy security does exist. To deal with the privacy issues in data mining, a sub field of data mining, referred to as privacy preserving data mining (PPDM) has gained a great development in recent years. The objective of PPDM is to safeguard sensitive information from unsolicitedor unsanctioned disclosure,and meanwhile,preserve the utility of the data.The consideration of PPDM is two-fold. First, sensitive raw data, such as individual’s ID card number and cell phone number, should not be directly used for mining. Second, sensitive mining results whose disclosure will result in privacy violation should be excluded.After the pioneering work of Agrawaletal.[3],[4], numerous studies on PPDM have been conducted [5]–[7].