SMedIR: secure medical image retrieval framework with ConvNeXt-based indexing and searchable encryption in the cloud

Arun Amaithi Rajan, Vetriselvi V, Mayank Raikwar, Reshma Balaraman
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Abstract

The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data.
SMedIR:基于 ConvNeXt 的索引和可搜索云加密的安全医学图像检索框架
医学影像具有敏感性,未经授权的修改可能造成严重后果,包括数据泄露和诊断不准确,因此医学影像的安全性和隐私性至关重要。本文介绍了一种从存储在第三方云上的加密图像中进行无损医学图像检索的方法。所提出的方法采用以对称完整性为中心的图像加密方案,利用多种混沌映射和加密哈希技术,确保无损图像重建。医学图像首先由图像所有者进行加密,然后利用深度散列技术,以 ConvNeXt 网络为骨干,并行将其转换为封装基本特征的散列码。为确保索引隐私,这些散列码以可搜索的方式进行加密。加密医学影像和安全索引随后上传到云存储。经授权的医学影像用户可通过提交查询图像请求类似的医学影像用于诊断,并由此生成搜索陷阱门并发送到云端。检索过程包括对加密索引进行安全的相似图像搜索,然后对检索到的图像进行解密和完整性验证。所提出的方法已在三个标准医疗数据集上进行了严格测试,结果表明与标准基线相比,检索准确率提高了 5-20%。正式的安全分析和实验结果表明,所提出的方案具有更高的安全性和检索准确性,是医疗图像数据加密存储和安全检索的有效解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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