ZK-DPPS: A Zero-Knowledge Decentralised Data Sharing and Processing Middleware

TL;DR

ZK-DPPS introduces a privacy-preserving framework for decentralized data sharing in supply chains, combining FHE and SMPC to avoid traditional ZKPs, enhancing efficiency and flexibility in IoT-driven environments.

Contribution

It presents a novel zero-knowledge communication system that replaces traditional ZKPs with FHE and SMPC, improving privacy and efficiency in decentralized supply chain data sharing.

Findings

Successfully demonstrates privacy preservation in supply chain simulation

Reduces computational overhead compared to ZKP-based methods

Ensures data confidentiality during processing and sharing

Abstract

In the current digital landscape, supply chains have transformed into complex networks driven by the Internet of Things (IoT), necessitating enhanced data sharing and processing capabilities to ensure traceability and transparency. Leveraging Blockchain technology in IoT applications advances reliability and transparency in near-real-time insight extraction processes. However, it raises significant concerns regarding data privacy. Existing privacy-preserving approaches often rely on Smart Contracts for automation and Zero Knowledge Proofs (ZKP) for privacy. However, apart from being inflexible in adopting system changes while effectively protecting data confidentiality, these approaches introduce significant computational expenses and overheads that make them impractical for dynamic supply chain environments. To address these challenges, we propose ZK-DPPS, a framework that ensures zero-knowledge communications without the need for traditional ZKPs. In ZK-DPPS, privacy is preserved through a combination of Fully Homomorphic Encryption (FHE) for computations and Secure Multi-Party Computations (SMPC) for key reconstruction. To ensure that the raw data remains private throughout the entire process, we use FHE to execute computations directly on encrypted data. The "zero-knowledge" aspect of ZK-DPPS refers to the system's ability to process and share data insights without exposing sensitive information, thus offering a practical and efficient alternative to ZKP-based methods. We demonstrate the efficacy of ZK-DPPS through a simulated supply chain scenario, showcasing its ability to tackle the dual challenges of privacy preservation and computational trust in decentralised environments.