Zero-Trust Blockchain Privacy Solution with Homomorphic Encryption
Source Title: Zero-Trust Learning, DOI Link
View abstract ⏷
To tackle the privacy protection and efficiency challenges within the block-chain domain, this chapter introduces a privacy-enhancing solution for copyright blockchains, integrating lightweight homomorphic encryption and zero-knowledge proofs. This innovative approach enhances homomorphic encryption algorithms to streamline key generation and encryption processes while incorporating zero-trust security principles to curtail unnecessary homomorphic operations. Following the application of lightweight homomorphic encryption, sensitive data is transformed into ciphertext and securely added to the blockchain ledger by nodes authorized for accounting purposes. This solution not only rectifies the inherent drawbacks of complete data transparency in blockchain networks but also enhances operational efficiency. Security analysis underscores its qualities, including resistance to tampering and data privacy preservation. Through both performance simulations and theoretical deductions, the paper demonstrates that this approach mitigates efficiency challenges related to the distribution, sharing, and computation of private data in ciphertext form. Ultimately, this proposed methodology proves more effective in upholding customer privacy than traditional digital copyright models