TY - GEN
T1 - Privacy preserving frequent pattern mining on multi-cloud environment
AU - Tai, Chih Hua
AU - Huang, Jen Wei
AU - Chung, Meng Hao
PY - 2013
Y1 - 2013
N2 - As the age of big data evolves, outsourcing of data mining tasks to multi-cloud environments has become a popular trend. To ensure the data privacy in outsourcing of mining tasks, the concept of support anonymity was proposed to hide sensitive information about patterns. Existing methods that tackle the privacy issues, however, do not address the related parallel mining techniques. To fill this gap, we refer to a pseudo-taxonomy based technique, called as k-support anonymity, and improve it into multi-cloud environments. This has several advantages. First, outsourcing to multi-cloud environments can meet the requirement of great computational resources in big data mining, and also parallelize the mining tasks for better efficiency. Second, the data that we send out to a cloud can be partial. An assaulter who gets the data in one cloud can never re-construct the original data. That means it is more difficult for an assailant to violate the privacy in outsourced data. Experimental results also demonstrated that our approaches can achieve good protection and better computation efficiency.
AB - As the age of big data evolves, outsourcing of data mining tasks to multi-cloud environments has become a popular trend. To ensure the data privacy in outsourcing of mining tasks, the concept of support anonymity was proposed to hide sensitive information about patterns. Existing methods that tackle the privacy issues, however, do not address the related parallel mining techniques. To fill this gap, we refer to a pseudo-taxonomy based technique, called as k-support anonymity, and improve it into multi-cloud environments. This has several advantages. First, outsourcing to multi-cloud environments can meet the requirement of great computational resources in big data mining, and also parallelize the mining tasks for better efficiency. Second, the data that we send out to a cloud can be partial. An assaulter who gets the data in one cloud can never re-construct the original data. That means it is more difficult for an assailant to violate the privacy in outsourced data. Experimental results also demonstrated that our approaches can achieve good protection and better computation efficiency.
UR - http://www.scopus.com/inward/record.url?scp=84885964480&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885964480&partnerID=8YFLogxK
U2 - 10.1109/ISBAST.2013.41
DO - 10.1109/ISBAST.2013.41
M3 - Conference contribution
AN - SCOPUS:84885964480
SN - 9780769550107
T3 - Proceedings - 2013 International Symposium on Biometrics and Security Technologies, ISBAST 2013
SP - 235
EP - 240
BT - Proceedings - 2013 International Symposium on Biometrics and Security Technologies, ISBAST 2013
T2 - 2013 International Symposium on Biometrics and Security Technologies, ISBAST 2013
Y2 - 2 July 2013 through 5 July 2013
ER -