@inproceedings{f2b36377ab644ed4974dd82106eb23dd,
title = "A Scalable Privacy Preserving System for Open Data",
abstract = "The citizen considers that data source collecting by the government can be released for more diversity usage. However, to archive the open data dream, sensitive data potentially could be published after the proper privacy preserving processing. In this paper, we present a scalable privacy preserving system for open/big data which leverages K-anonymity algorithm and Hadoop framework. We use an experiment data (i.e., 10 TB) to show our system can handle the high-volume data when increasing the system resource. It is an essential factor for the Government to publish the data with privacy preserving processing.",
keywords = "K-anonymity, big data, open data, privacy preserving",
author = "Yeh, {Chao Chun} and Wang, {Pang Chieh} and Pan, {Yu Hsuan} and Kao, {Ming Chih} and Shih-Kun Huang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Computer Symposium, ICS 2016 ; Conference date: 15-12-2016 Through 17-12-2016",
year = "2017",
month = feb,
day = "16",
doi = "10.1109/ICS.2016.0069",
language = "English",
series = "Proceedings - 2016 International Computer Symposium, ICS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "312--317",
booktitle = "Proceedings - 2016 International Computer Symposium, ICS 2016",
address = "美國",
}