TY  - JOUR
T1  - Models for Privacy-preserving Data Publishing : A Survey
AU  - Kim, Jongseon 
AU  - Jung, Kijung 
AU  - Lee, Hyukki 
AU  - Kim, Soohyung 
AU  - Kim, Jong Wook 
AU  - Chung, Yon Dohn 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 
KW  - data privacy
KW  - privacy model
KW  - anonymization
KW  - privacy-preserving data publishing
AB  - In recent years, data are actively exploited in various fields. Hence, there is a strong demand for sharing and publishing data. However, sensitive information regarding people can breach the privacy of an individual. To publish data while protecting an individual’s privacy with minimal information distortion, the privacy- preserving data publishing(PPDP) has been explored. PPDP assumes various attacker models and has been developed according to privacy models which are principles to protect against privacy breaching attacks. In this paper, we first present the concept of privacy breaching attacks. Subsequently, we classify the privacy models according to the privacy breaching attacks. We further clarify the differences and requirements of each privacy model.