Blockchain-Based Secure Trading of Remote Sensing Images With Privacy-Preserving Retrieval and Zero-Watermarking
Baowei Wang, Xuekang Yang, Jun Wu, Pengfei Yang, Ruohan Meng · IEEE Transactions on Geoscience and Remote Sensing · 2025
Remote sensing data, due to its sensitive nature, requires secure trading mechanisms to prevent leakage and misuse. While blockchain offers potential for secure transactions, existing systems lack granularity in buyer descriptions and accurate data provision of sellers in pre-transaction and fail to ensure post-transaction supervision and copyright protection. To address these limitations, this paper presents a blockchain-based secure transaction framework for remote sensing images, covering the entire transaction lifecycle. The framework begins by securely encrypting and storing images to ensure data integrity and confidentiality. During transactions, an enhanced product quantization method is applied to enable efficient image retrieval. Combined with searchable symmetric encryption, it ensures privacy preservation, improves accuracy, and safeguards sensitive data. In post-transaction, a novel zero-watermarking approach ensures robust copyright protection by embedding personalized watermarks that fuse stable image features with transaction-specific information, facilitating reliable copyright verification. Experimental results demonstrate that our transaction scheme achieves an average retrieval accuracy exceeding 90%, and the average Gas cost is less than 100,000, while the constructed zero-watermarking exhibits strong uniqueness and robustness. These findings confirm the scheme’s effectiveness in enabling fine-grained image transactions and reliable copyright protection, supporting a secure and fair trading process.