Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third...Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.展开更多
多方隐私集合交集(Multiparty Private Set Intersection,MPSI)协议允许多个参与方各自持有私有集合,在不泄露除交集以外任何信息的前提下,安全计算所有集合的交集,在泄露凭证检查、金融反欺诈和联邦学习等领域具有广泛应用。然而,现有...多方隐私集合交集(Multiparty Private Set Intersection,MPSI)协议允许多个参与方各自持有私有集合,在不泄露除交集以外任何信息的前提下,安全计算所有集合的交集,在泄露凭证检查、金融反欺诈和联邦学习等领域具有广泛应用。然而,现有非平衡隐私集合交集协议主要针对两方参与场景,缺乏针对多方参与场景的高效解决方法。为了解决MPSI及其变体协议在非平衡数据集上效率低下的问题,本文提出一种非平衡双中心零共享的方法。该方法结合零共享和不经意键值存储技术,将多方非平衡计算归约为两方非平衡计算,有效降低了通信和计算开销。然后,通过将该方法和两方非平衡隐私集合交集及其变体协议相结合,构建了一种新的高效且可扩展的多方非平衡隐私集合交集(Multiparty Unbalanced Private Set Intersection, MUPSI)及其变体协议。实验结果表明,在相同条件下,客户端的集合规模为2^(10),服务器端的集合规模为2^(27)时,本文提出的MUPSI协议的服务器端在线阶段耗时比目前最优的协议缩短约20%。此外,在32个参与方的场景下,客户端的集合规模为2^(10),服务器端的集合规模为2^(27)时,客户端在线阶段耗时约10秒,验证了该协议在大规模参与方以及集合规模差异显著场景下的有效性。展开更多
基金supported by the NationalNatural Science Foundation of China(No.61862041).
文摘Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.
文摘多方隐私集合交集(Multiparty Private Set Intersection,MPSI)协议允许多个参与方各自持有私有集合,在不泄露除交集以外任何信息的前提下,安全计算所有集合的交集,在泄露凭证检查、金融反欺诈和联邦学习等领域具有广泛应用。然而,现有非平衡隐私集合交集协议主要针对两方参与场景,缺乏针对多方参与场景的高效解决方法。为了解决MPSI及其变体协议在非平衡数据集上效率低下的问题,本文提出一种非平衡双中心零共享的方法。该方法结合零共享和不经意键值存储技术,将多方非平衡计算归约为两方非平衡计算,有效降低了通信和计算开销。然后,通过将该方法和两方非平衡隐私集合交集及其变体协议相结合,构建了一种新的高效且可扩展的多方非平衡隐私集合交集(Multiparty Unbalanced Private Set Intersection, MUPSI)及其变体协议。实验结果表明,在相同条件下,客户端的集合规模为2^(10),服务器端的集合规模为2^(27)时,本文提出的MUPSI协议的服务器端在线阶段耗时比目前最优的协议缩短约20%。此外,在32个参与方的场景下,客户端的集合规模为2^(10),服务器端的集合规模为2^(27)时,客户端在线阶段耗时约10秒,验证了该协议在大规模参与方以及集合规模差异显著场景下的有效性。
