Title :
New Data Publishing Framework in the Big Data Environments
Author :
Jun Yang ; Zheli Liu ; Chunfu Jia ; Kai Lin ; Zijing Cheng
Author_Institution :
Coll. of Comput. & Control Eng., Nankai Univ., Tianjin, China
Abstract :
The traditional data publishing methods will remove the sensitive attributes and generate the abundant records to achieve the goal of privacy protection. In the big data environment, they cannot satisfy some data mining tasks with privacy considerations. This paper provides a new data publishing framework. It can preserve the data integrity, i.e., the original data structure is preserved, and it doesn´t require deleting any attribute and adding k-times data to achieve anonymity.
Keywords :
Big Data; cryptography; data integrity; data mining; data privacy; data structures; anonymity; big data environment; data integrity preservation; data mining; data publishing framework; data publishing method; data structure preservation; decyption; encyption; privacy considerations; privacy protection; Data mining; Databases; Encryption; Privacy; Publishing; data publishing; big data; format-preserving encryption; data mask;
Conference_Titel :
P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2014 Ninth International Conference on
Conference_Location :
Guangdong
DOI :
10.1109/3PGCIC.2014.139