Blockchain electronic evidence sharing based on improved ciphertext policy attribute encryption

With the advancement of the information age, the widespread application of electronic evidence in fields such as justice and finance has brought new challenges. Although existing blockchain electronic evidence sharing schemes have immutability and transparency, they still have shortcomings in access control, data privacy protection, and efficiency. In addition, traditional attribute encryption strategies lack effective revocation mechanisms and cannot fully protect privacy when implementing fine-grained access control. Therefore, in order to address the above limitations, a blockchain electronic evidence sharing scheme based on an improved ciphertext policy attribute encryption combined with zero knowledge proof technology has been proposed. The research innovatively introduces revocable ciphertext strategy encryption, which addresses the security risks caused by decryption key leakage through revocation function, ensuring the secure storage and sharing of electronic evidence. Meanwhile, the study also improved the PBFT consensus algorithm to enhance its performance in handling large volumes of transactions. The results showed that the storage TPS of the research model reached 492, and the query TPS reached 655. The computational cost of improving the PBFT consensus algorithm is 1.94 × 10 4 , and the maximum computational cost of the electronic evidence access control model based on zero knowledge proof is 509. Compared with traditional blockchain based electronic evidence sharing methods, the improved method not only enhances storage and sharing efficiency, but also further strengthens privacy protection capabilities by combining zero knowledge proof technology. In summary, the research method effectively achieves secure sharing and privacy protection of electronic evidence on blockchain, providing support and reference for electronic evidence storage in fields such as justice and finance. However, there are still challenges in terms of scalability and data storage in the research, so algorithms can be optimized in the future to further improve the application scope of the system.