OBFP: Optimized Blockchain-Based Fair Payment for Outsourcing Computations in Cloud Computing

Chao Lin, Debiao He, Xinyi Huang, Kim-Kwang Raymond Choo

Abstract: Outsourcing computations have been widely used to meet the growing computing demands, although achieving trust in an untrusted (or a zero-trust) environment can be challenging in practice. Fair payment, a candidate solution, can potentially facilitate fair trading among outsourcing computation participants such as users and workers. However, most existing solutions including traditional e-cash-based or blockchain-based, may potentially compromise the worker's fairness (i.e., does not achieve robust fairness, since trusted third parties are required during the trading process), or involve heavy zero-knowledge proofs (ZKPs, with significant computation costs). To mitigate these limitations, we propose a system model of an optimized blockchain-based fair payment (OBFP) for outsourcing computations. Then, we construct a ZKP-free solution based on blockchain by combining any secure commitment, accumulator, and symmetric encryption schemes, as well as a hash function. To demonstrate the utility of our proposed OBFP system, we provide security analysis, performance evaluation and a comparison with existing popular solutions. Specifically, the cryptographic tools are instantiated as commitment (Perdesen commitment), accumulator (RSA-based accumulator), and symmetric encryption (a concrete scheme with the indistinguishability under chosen-plaintext attack (IND-CPA) security), and a hash function (Keccak-256). The prototype is implemented in COSBench and Remix to analyze cloud scalability and concurrency, as well as gas cost.