@article {478,
	title = {Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases},
	journal = { IEEE Systems Journal},
	year = {2013},
	abstract = {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{\textquoteright}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.},
	doi = {10.1109/JSYST.2012.2221854},
	author = {Fosca Giannotti and L.V.S. Lakshmanan and Anna Monreale and Dino Pedreschi and Hui Wendy Wang}
}
@conference {481,
	title = {Privacy-preserving data mining from outsourced databases.},
	booktitle = { the 3rd International Conference on Computers, Privacy, and Data Protection: An element of choice },
	year = {2011},
	month = {2011},
	abstract = {Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-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 outsourced database and the knowledge extract from it by data mining are considered private property of the 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 a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.},
	doi = {10.1007/978-94-007-0641-5_19},
	author = {Fosca Giannotti and L.V.S. Lakshmanan and Anna Monreale and Dino Pedreschi and Hui Wendy Wang}
}
