Verifiable inner product computation on outsourced database for authenticated multi-user data sharing

With the rapid development of cloud computing, the practical applications such as the machine learning based on the outsourced data have been investigated in the data sharing setting. In machine learning, the inner product is a necessary primitive to analyze the description statistics. However, the inner product computation in selective data sharing setting has not been fully considered. For this fact, we propose a verifiable inner product computation scheme based on Inner Product Functional Encryption (IPFE). IPFE is employed to preserve the outsourced data privacy and restrict the computation on the outsourced data to be inner product. To achieve the key privacy and result privacy, we transform the secret key into blinded form, which in turn results in a blinded result. With the aim of implementing access control over the data user and outsourced data, we design to let cloud server perform the authentication procedures before computing inner product. This can also eliminate most computational overhead resulting from the unauthorized data user and undesired data. As a result, only the authorized data user can obtain the inner product computed on the designated outsourced data. The proposed scheme is proved to be secure under the authentication model and the result unforgeability model. The performance evaluation shows that the proposed scheme is feasible. To achieve a better security level, the proposed scheme is extended to be secure against the corrupted cloud server. (C) 2020 Elsevier Inc. All rights reserved.