01777nas a2200145   4500008003900000245008900039210006900128520121300197100002101410700002201431700001901453700002001472700002101492856011801513       2013                          d00aPrivacy-Preserving Mining of Association Rules From Outsourced Transaction Databases0 aPrivacyPreserving Mining of Association Rules From Outsourced Tr3 aSpurred 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.1 aGiannotti, Fosca1 aLakshmanan, L V S1 aMonreale, Anna1 aPedreschi, Dino1 aWang, Hui, Wendy  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-mining-association-rules-outsourced-transaction-databases01493nas a2200157   4500008003900000245006200039210006000101260000900161520096900170100002101139700002201160700001901182700002001201700002101221856009301242       2011                          d00aPrivacy-preserving data mining from outsourced databases.0 aPrivacypreserving data mining from outsourced databases  c20113 aSpurred 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.1 aGiannotti, Fosca1 aLakshmanan, L V S1 aMonreale, Anna1 aPedreschi, Dino1 aWang, Hui, Wendy  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-data-mining-outsourced-databases