Privacy-preserving fair outsourcing polynomial computation without FHE and FPR

With the development of cloud computing, verifiable outsourcing computation (VC) has received much more attention. The polynomial is a fundamental mathematical function with widespread applications. Plenty of VC schemes for polynomials have been proposed recently. However, most previous schemes focus on ensuring that the client can get a valid result returned by the cloud service provide (CSP) before payment, while often ignoring the CSP’s interest. To the best of our knowledge, Guan et al. (2021) proposed a pioneering framework for building fair outsourcing polynomial computation, which serves as the state of the art. However, it discloses the privacy of outsourced polynomials, inputs, and outputs. Furthermore, it suffers from a false positive rate (FPR) in the verification phase due to the sampling technique. As a result, it breaks the fairness between the client and the CSP.

To solve these problems, we propose a privacy-preserving fair outsourcing polynomial computation without FPR. To avoid expensive Fully Homomorphic Encryption (FHE), we utilize Paillier encryption and blind technique to ensure privacy. Our proposed scheme can guarantee fairness with an overwhelming probability by applying the SGX technique. The comprehensive performance evaluation and extensive simulations show that our protocol is more practical in cloud computing.