Privacy-preserving blockchain systems based on homomorphic encryption: design and challenges

Abstract

With the widespread adoption of blockchain technology in domains such as finance, healthcare, and supply chain management, its transparent and immutable ledger mechanism has improved traceability and trustworthiness while simultaneously introducing potential risks of user privacy leakage. Homomorphic encryption (HE), as a cryptographic technique that enables computations to be performed directly on ciphertexts, has emerged as a promising approach for achieving data availability without exposing sensitive information in blockchain-based environments. This paper investigates recent advances in the integration of HE with blockchain privacy protection, with particular emphasis on representative schemes employing fully homomorphic encryption (FHE) and partially homomorphic encryption (PHE) in scenarios including privacy-preserving smart contracts, federated learning, and electronic voting. Furthermore, existing studies are comparatively discussed from the perspectives of computational characteristics, deployment architectures, and scalability considerations, followed by a discussion of the major challenges that may hinder practical deployment. Finally, potential research directions, including lightweight FHE, hardware-assisted acceleration, and the integration of HE with zero-knowledge proofs (ZKPs), are discussed to provide references for the future design of blockchain privacy-preserving systems.