Using Homomorphic Encryption to Compute Privacy Preserving Data Mining in a Cloud Computing Environment

摘要

Cloud computing refers to an information technology infrastructure where data and software are stored and processed in a remote data center, accessible as a service through the Internet. Typical data centers within these fields are large, complex and often noisy. Further-more, privacy preserving data mining is an important challenge. It is required to protect the confidentiality of data sources during the extraction of frequent closed patterns. In fact, no site should be able to learn contents of a transaction at any other site. The work carried out in this paper deals with this problem. In this context, we suggest an approach that combines the extraction of frequent closed patterns in a distributed environment such as the cloud. We aim at maintaining the privacy of the sites during the data mining task in a cloud environment based on homomorphic encryption. The Simulation results and performance analysis show that our mechanism requires less communication and computation overheads. It can effectively preserve data privacy, check data integrity, and ensures high data transmission efficiency.