Encrypted Association Rule Mining for Outsourced Data Mining

Rule mining, for discovering valuable relations between items in large databases, has been a popular and well researched method for years. However, such old but important technique faces huge challenges and difficulties in the era of cloud computing although which affords both storage and computing scalability: 1) data are outsourced to a cloud due to data explosion and high storage and management cost, 2) moreover, data are usually encrypted first before being outsourced for privacy´s sake. Existing privacy-preserving rule mining methods only assume a distributed model where every data owner holds the self data without encryption and together follow a secure protocol to perform rule mining. To address this limitation, we propose a novel Protocol for Outsourced Rule Mining (PORM) in this paper. PORM performs rule mining in a cloud environment where data are both encrypted and outsourced. We formally proved that PORM is both correct and secure, and we also extended PORM to the multiple-user scenario.