Communication-efficient outsourced privacy-preserving classification service using trusted processor

Machine learning (ML) classification has been one of the most important techniques of popular Internet services that aim to provide accurate predictions of data by means of a classifier model. In a machine-learning-as-a-service (MLaaS) system, the service provider allows classifier owners to upload their classifier models and charge other users for access on a pay-per-query basis, so that a user can query a classifier with data instances and then obtain their classification results. However, in a traditional way, either stored classifiers or queried data, which are potentially sensitive and economical, will be exposed by the service. Due to privacy concerns, both the classifiers and the data should remain confidential. In this paper, we propose a novel scheme to enable a classifier owner to out-sourcely store his/her classifier model on a cloud server for users' queries, while protecting the confidentiality of classifier and data. We adopt a trusted processor to design efficient classification protocols for two concrete classifiers respectively. For the communicational efficiency, users only need to interact with the server no more than twice for each query in our scheme. We implement the prototype of the scheme and conduct experiments in an Intel SGX enclave. The experimental results show that the scheme is practical. (C) 2019 Elsevier Inc. All rights reserved.