Data publishing Anonymity Algorithm Research Based on Clustering
- DOI
- 10.2991/ifmeita-16.2016.138How to use a DOI?
- Keywords
- (l, c) anonymous algorithm; Data publishing; Clustering; Personal privacy
- Abstract
Data publishing provides convenience for data exchange and data sharing. But at the same time, the issue of personal privacy information leakage has become increasingly prominent. Anonymous algorithm is one of the main technologies in data publishing environment to realize privacy protection, but most anonymity algorithm of all sensitive attributes values are treated equally, without considering their sensitivity and specific distribution. It is vulnerable to similar attacks and deviation of attack. The equivalence classes are established by clustering technique, and the different levels of privacy protection are defined for each sensitive attribute value. Using local heavy coding scheme on the identifier to anonymous, anonymity algorithm (l, c) based on clustering is put forward. Experimental results show that the proposed algorithm improves the availability of published data while protecting privacy.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yu Yang AU - Longjun Zhang PY - 2016/01 DA - 2016/01 TI - Data publishing Anonymity Algorithm Research Based on Clustering BT - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application PB - Atlantis Press SP - 758 EP - 762 SN - 2352-5398 UR - https://doi.org/10.2991/ifmeita-16.2016.138 DO - 10.2991/ifmeita-16.2016.138 ID - Yang2016/01 ER -