Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases

With recent trends in big data and cloud computing, data mining has also attracted considerable interest due to its potential to deal with distributed data in the cloud. However, existing data mining technologies may not be directly deployed as we need to avoid accidental privacy disclosure when data from different sources are mined. In this paper, we propose a secure and flexible cloud-assisted association rule mining over horizontally partitioned databases. Using the proposed scheme, data owners can provide their data and mine the association rules in the cloud flexibly, while being assured of minimal risks of privacy leakage. We then show that our proposed scheme achieves privacy-preserving mining of association rules, and provides resilience against collusion attacks. A comparative summary demonstrates that the proposed scheme is more efficient, in terms of computational costs, relative to several existing homomorphic-encryption-based schemes. (C) 2016 Elsevier Inc. All rights reserved.