A Scalable Privacy Preserving System for Open Data
Chao-Chun Yeh, Pang-Chieh Wang, Yu-Hsuan Pan, Ming-Chih Kao, Shih-Kun Huang · 2016
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.