Enhancing Privacy and Integrity in Computing Services Provisioning Using Blockchain and Zk-SNARKs

【Author】 Ballesteros-Rodriguez, Alberto; Sanchez-Alonso, Salvador; Sicilia-Urban, Miguel-Angel

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】The widespread integration of on-demand services founded on proprietary algorithms into various software applications has ushered into a new era of advanced service capabilities. However, using these services entails disclosing information by the customer, not only during the payment process but also when using the service, where certain personal information must be shared to obtain a more personalized service. This practice potentially exposes users to increased security risks in case of data security breaches. In this paper, we introduce a novel framework aimed at enhancing client privacy and ensuring service integrity within the context of computing services that rely on proprietary algorithms. A blockchain-based approach is proposed to enhance user privacy throughout service provision, encompassing both the payment process and the verification of the provided service. Our proposal leverages properties of distributed ledger networks to improve user privacy during payment transactions and incorporates a verification system using zero-knowledge proofs on blockchain to validate the integrity of the contracted service. Finally, we analyze the privacy, overhead, and performance aspects of the framework, employing custom proprietary algorithms. We illustrate this through examples of Convolutional Neural Networks with multiple layers, undisclosed to the client. This emphasizes the potential benefits of its applicability for both service providers and clients.

【Keywords】Privacy; Blockchains; Computational modeling; Data privacy; Peer-to-peer computing; Training; Software as a service; Blockchain; proprietary algorithms; service verification; privacy; zk-SNARKs

【发表时间】2024

【收录时间】2024-09-19

【文献类型】实验仿真

【主题类别】

区块链技术-核心技术-零知识证明