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 verifiability. In this paper, we present an efficient 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.
Highlights
Cloud computing, with its high data processing capabilities, is important to all applications that require high processing costs such as data processing machine learning [1]
We introduce the divisible computational Diffie-Hellman assumption, which concerns the difficulty of measuring the discrete logarithm in cyclic groups
Cloud computing security is still considered as a major issue, especially with privacy-preserving of the data providers to the third party systems
Summary
With its high data processing capabilities, is important to all applications that require high processing costs such as data processing machine learning [1]. A. Hassan et al.: Efficient Outsourced Privacy Preserving Machine Learning Scheme With Public Verifiability public cloud as well as perform analytic services of the cloud provider’s on the encrypted data. To overcome the above issues, it is important to propose an effective privacy scheme based on machine learning given multiple data providers. Li et al [23] proposed a privacy-preserving machine learning framework dealing with multiple data providers This solution came with a high computational cost due to the dependence on integer factorization in their proposed framework. This paper aims to overcome the mentioned above issues by proposing an efficient privacy-preserving machine learning scheme for multi-providers data in the cloud system.
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