Secure multi-label data classification in cloud by additionally homomorphic encryption

Abstract

In many applications, an instance is associated with a set of labels. Nevertheless, traditional classification algorithm generally assumes an instance has a label, and cannot be suitable for multi-label instance classification. Multi-label learning is thus put forward to predict the associated labels for instance with multiple unclassified labels. Nowadays, many multi-label learning approaches have been proposed, unfortunately, all of the existing multi-label learning approaches did not consider the issue of protecting the sensitive information of private instances. In this paper, we propose a scheme for secure multi-label classification over encrypted data in cloud. Our scheme can outsource the multi-label classification task to the cloud servers which dramatically reduce the storage and computation burden of data owner and data users. Based on the theoretical proof, our scheme can protect the privacy information of data owner and data users, the cloud servers cannot learn anything useful about the input data and output multi-label classification results. Additionally, we analyze our computation complexity and communication overheads in detail, and leverage simulation experiments to evaluate the computation time of our proposed scheme.