This study proposes an privacy-protection method for blockchain transactions based on lightweight homomorphic encryption, aiming to ensure the security of transaction data and user privacy, and improve transaction efficiency. We have built a blockchain infrastructure and, based on its structural characteristics, adopted zero-knowledge proof technology to verify the legitimacy of data, ensuring the authenticity and accuracy of transactions from the application end to the smart-contract end. On this basis, the Paillier algorithm is used for key generation, encryption, and decryption, and intelligent protection of blockchain transaction privacy is achieved through a secondary encryption mechanism. The experimental results show that this method performs well in privacy and security protection, with a data leakage probability as low as 2.8%, and can effectively defend against replay attacks and forged-transaction attacks. The degree of confusion remains above 0.9, with small fluctuations and short running time under different key lengths and moderate CPU usage, achieving lightweight homomorphic encryption. This not only ensures the security and privacy of transaction data in blockchain networks, but also reduces computational complexity and resource consumption, better adapting to the high-concurrency and low-latency characteristics of blockchain networks, thereby ensuring the efficiency and real-time performance of transactions.

Blockchain technology, characterized by its immutability and decentralization, enables the creation of permanent and tamper-resistant records once data are uploaded, making it widely applicable in scenarios requiring data authenticity and reliability. However, the immutability of on-chain data poses significant security risks, as erroneous or illegal data become difficult to correct or remove once recorded. Editable blockchain technology offers a potential solution for on-chain data modification. Nevertheless, existing approaches face several challenges, including the impact of malicious nodes on the security and efficiency of data modification, excessive centralization in the management of modification rights and trapdoor keys, and cumulative issues in reputation-based traditional node grouping methods. To address these challenges, this study proposes an RE-TNG (Reputation Evaluation-Twice Node Grouping) node selection method and an editable blockchain scheme based on it. The RE-TNG method employs a two-stage grouping process following reputation-based node ranking. The first grouping stage uses a Fibonacci sequence-based rule to mitigate the issue of cumulative reputation values over time. The second grouping stage selects high-reputation nodes within groups to ensure the trustworthiness of selected nodes. Trapdoor keys are collaboratively generated by the high-reputation node group, achieving decentralized trapdoor management. Modification nodes are randomly chosen from the high-reputation group, ensuring both integrity and decentralization in modification authority. Comparative analyses and experimental evaluations against traditional random node selection and grouping methods demonstrate the feasibility of the proposed scheme, showcasing a superior performance in terms of security and modification efficiency.