An Efficient Outsourced Privacy Preserving Machine Learning Scheme With Public Verifiability

Abstract

Cloud computing has been widely applied in numerous applications for storage and data analytics tasks. However, cloud servers engaged through a third party cannot be fully trusted by multiple data users. Thus, security and privacy concerns become the main obstructions to use machine learning services, especially with multiple data providers. Additionally, some recent outsourcing machine learning schemes have been proposed in order to preserve the privacy of data providers. Yet, these schemes cannot satisfy the property of public veri ability. In this paper, we present an ef cient privacy-preserving machine learning scheme for multiple data providers. The proposed scheme allows all participants in the system model to publicly verify the correctness of the encrypted data. Furthermore, a unidirectional proxy re-encryption (UPRE) scheme is employed to reduce the high computational costs along with multiple data providers. The cloud server embeds noise in the encrypted data, allowing the analytics to apply machine learning techniques and preserve the privacy of data providers' information. The results and experiments tests demonstrate that the proposed scheme has the ability to reduce computational costs and communication overheads.