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  • Open Access

    ARTICLE

    A Blockchain-Based Efficient Verification Scheme for Context Semantic-Aware Ciphertext Retrieval

    Haochen Bao1, Lingyun Yuan1,2,*, Tianyu Xie1,2, Han Chen1, Hui Dai1

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-30, 2026, DOI:10.32604/cmc.2025.069240 - 10 November 2025

    Abstract In the age of big data, ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge. Traditional searchable encryption schemes face difficulties in handling complex semantic queries. Additionally, they typically rely on honest but curious cloud servers, which introduces the risk of repudiation. Furthermore, the combined operations of search and verification increase system load, thereby reducing performance. Traditional verification mechanisms, which rely on complex hash constructions, suffer from low verification efficiency. To address these challenges, this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification. Building on existing… More >

  • Open Access

    ARTICLE

    A New Method of Image Restoration Technology Based on WGAN

    Wei Fang1,2,*, Enming Gu1, Weinan Yi1, Weiqing Wang1, Victor S. Sheng3

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 689-698, 2022, DOI:10.32604/csse.2022.020176 - 25 October 2021

    Abstract With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding More >

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