Privacy-Preserving and Publicly Verifiable Matrix Multiplication

【Author】 Liu, Jing; Zhang, Liang Feng

【Source】IEEE TRANSACTIONS ON SERVICES COMPUTING

【影响因子】11.019

【Abstract】Outsourcing computations allows the resource-constrained clients (such as, IoT devices) to offload heavy computations to powerful cloud servers. At the same time, it brings many challenges such as data privacy, result verification and fair payment. In this article, we propose a privacy-preserving multi-function verifiable computation (MFVC) model and construct an MFVC scheme for outsourcing matrix multiplication computations (MMC), which have many real-life applications, such as machine learning. Our scheme keeps one of the matrices in MMC semantically secure and allows a public verification of the server's work. In particular, the client's work is faster than the native MMC for square matrices of order $\geq 7000$& GE;7000, while the best existing scheme requires matrices of order $\geq 270000$& GE;270000. We also propose a smart contract-based framework that equips the proposed scheme with fair payment property. By offloading the verification to the blockchain, the client's work is faster than the native MMC for square matrices of order $\geq 6000$& GE;6000.

【Keywords】Servers; Computational modeling; Outsourcing; Smart contracts; Privacy; Data privacy; Blockchains; Matrix multiplication; outsourcing computation; Index Terms; smart contract; blockchain

【发表时间】2023 MAY-JUN

【收录时间】2023-07-29

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