Privacy-preserving Mining of Association Rules from Outsourced Transaction Databases

Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of datamining-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 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 position paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose a scheme for privacy preserving outsourced mining and show that the owner can recover the true patterns as well as their support by maintaining a compact synopsis.