Blockchain-aided secure and fair multi-view data outsourcing computation scheme

With the widespread deployment of smart sensors, multi-view data has been widely used. Accordingly, multi-view processing algorithms are increasingly researched, among which the cluster-weighted kernel k-means method is an effective approach to dig up information of different views. However, large-scale multi-view data make it difficult to conduct processing algorithms. Therefore, outsourcing complex computations to servers based on privacy-preserving techniques is an effective solution that enables efficient multi-view data analysis. In previous secure outsourcing schemes, the efficiency of the outsourcing process and the fairness of outsourcing transactions are still challenging issues that have not been addressed. In this paper, we propose a blockchain-aided secure and fair multi-view data outsourcing computation scheme. We present an efficient matrix encryption method utilizing a novel secret key matrix to complete cluster-weighted kernel k-means algorithm securely. Different from previous works, we first apply the sparse symmetric orthogonal matrix to encrypt and decrypt sensitive data matrices, which avoids inverse or transposed secret key matrix computation and enhances the efficiency of the outsourcing process. Additionally, we introduce smart contracts to achieve fair outsourcing transactions aided by blockchain. We verify the returned result with the assistance of verifiers based on encrypted data, which improves the efficiency and security of outsourcing transactions. The experimental results indicate that our scheme is 4.72% to 8.52% superior to the state-of-the-art matrix outsourcing computation schemes and achieves 55.79% to 91.95% efficiency improvement compared to the original multi-view data processing method.