ABSTRACT Personal information exposed in the Internet is increasing by t he public data opening and sharing, vitalization of SNS(Social Network Service) and growth of information shared between users . Exposed personal information in the Internet can infringe upon targeted users using linkage attack or background attack. To prevent these attack De-identification models were appeared a few years ago. The ’k-anonymity’ has been introduced in the f irst place, and the ‘l-diversity’ and ’t-closeness’ have been followed up as solutions, and diverse algorithms have been bein g suggested for performance improvement nowadays. However, industry or public sectors actually needs a whole solution as a system for the de-identification process rather than performance of the de-identification algorithm. This paper explains a way of de-identification techique for ‘k-anonymity’, ‘ l-diversity’, and ’t-closeness’ algorithm using QI(Quasi-Identifier) grouping met hod in the relational database.Keywords: K-anonymity, L-diversity, T-closeness, De-identification Algorithms