@article{M9553836C,
title = "Models for Privacy-preserving Data Publishing : A Survey",
journal = "Journal of KIISE, JOK",
year = "2017",
issn = "2383-630X",
doi = "",
author = "Jongseon Kim,Kijung Jung,Hyukki Lee,Soohyung Kim,Jong Wook Kim,Yon Dohn Chung",
keywords = "data privacy,privacy model,anonymization,privacy-preserving data publishing",
abstract = "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."
}