An Improved Apriori Preserving Differential Privacy in the Framework of Spark

DOI

10.2991/cimns-16.2016.61How to use a DOI?

Keywords

spark; differential privacy; association analysis; pattern mining; association rule algorithm

Abstract

Aimed at the problem that traditional methods fail to deal with malicious attacks under arbitrary background knowledge during the process of massive data analysis, an improved Apriori algorithm preserving differential privacy, combining with Laplace mechanism to mine the pattern of sensitive information in framework of Spark is proposed. Furthermore, it's theoretically proved to meet -differential privacy in spark. Finally, experimental results show that guaranteeing availability, our proposed algorithm has advantages over privacy protection and satisfaction in aspects of time as well as efficiency. Most importantly, our algorithm shows a good application prospect in the analysis of data pattern mining preserving privacy protection. Also, it has better ability of privacy protection and timeliness under the premise of ensuring availability.

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/).

Download article (PDF)

TY  - CONF
AU  - Zhiqiang Gao
AU  - Longjun Zhang
AU  - Renyuan Hu
AU  - Qingpeng Li
AU  - Jihua Yang
PY  - 2016/09
DA  - 2016/09
TI  - An Improved Apriori Preserving Differential Privacy in the Framework of Spark
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 245
EP  - 247
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.61
DO  - 10.2991/cimns-16.2016.61
ID  - Gao2016/09
ER  -