Association Rule Mining, Privacy Preserving Outsourcing
There has been significant recent interest in the paradigm of data mining as-a-service. A company (data owner) lacking in proficiency or computational resources can outsource its mining needs to a third party service provider (server). In spite of this, both the items and the association rules of the outsourced database are considered private property of the company (data owner). To protect corporate or individuals 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, experiment evaluation of outsourcing the association rule mining task within a corporate privacy-preserving framework. Proposed an attack model based on background knowledge and devise a approach for privacy preserving outsourced mining.
Represented approach ensures that each transformed item is indistinguishable with respect to the attacker’s background knowledge, from at least k−1 other transformed items. These comprehensive experiments on a very large and real transaction database demonstrate that these techniques are effective, scalable, and protect privacy.