TY - JOUR
T1 - Privacy-preserving mining of association rules from outsourced transaction databases
AU - Giannotti, Fosca
AU - Lakshmanan, Laks V.S.
AU - Monreale, Anna
AU - Pedreschi, Dino
AU - Wang, Hui
PY - 2013
Y1 - 2013
N2 - Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker's background knowledge, from at least k-{1} other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.
AB - Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker's background knowledge, from at least k-{1} other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.
KW - Association rule mining
KW - privacy-preserving outsourcing
UR - http://www.scopus.com/inward/record.url?scp=84880569901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880569901&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2012.2221854
DO - 10.1109/JSYST.2012.2221854
M3 - Article
AN - SCOPUS:84880569901
SN - 1932-8184
VL - 7
SP - 385
EP - 395
JO - IEEE Systems Journal
JF - IEEE Systems Journal
IS - 3
M1 - 6365738
ER -