Secure Multi-label Classification over Encrypted Data in Cloud

Abstract

In multi-label (ML) learning, each training instance is associated with a set of labels to present its multiple semantic information, and the task is to predict the associated labels for each unclassified instance. Nowadays, many multi-label learning approaches have been proposed, unfortunately, all of the existing approaches did not consider the issue of protecting the privacy information. 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 can not learn anything useful about the input data and output multi-label classification results. Additionally, we evaluate our computation complexity and communication overheads in detail.