当前位置: X-MOL 学术IEEE Trans. Serv. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images
IEEE Transactions on Services Computing ( IF 8.1 ) Pub Date : 2022-02-09 , DOI: 10.1109/tsc.2022.3149847
Dan Zhu 1 , Hui Zhu 2 , Xiangyu Wang 3 , Rongxing Lu 4 , Dengguo Feng 5
Affiliation  

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.

中文翻译:

一种准确且隐私保护的外包医学影像检索方案

随着医学成像技术的快速发展,基于内容的(医学)图像检索(CBIR)可以辅助疾病诊断,受到学术界和工业界的广泛关注。然而,由于医疗图像中涉及患者的敏感信息,隐私保护 CBIR 是一个值得开发的挑战。尽管已经提出了几种隐私保护的CBIR方案,但它们只能抵抗已知背景攻击(KBA),不足以保护外包环境中的图像隐私。在本文中,针对上述挑战,我们首先设计了一种新颖的隐私保护马氏距离比较 (PMDC) 方法,以提高医学图像检索的准确性。然后,结合基于马氏距离的模糊C均值(FCM-M)算法,提出了一种名为TAMMIE的方案来实现对加密数据的准确和隐私保护的医学图像检索。借助 TAMMIE,图像所有者可以安全地将图像和索引外包给云服务器,查询用户可以向云服务器请求检索服务,同时保持查询的私密性。详细的安全分析表明,我们提出的方案在比KBA 更强的攻击下是安全的。此外,对两个真实世界数据集和一个合成数据集进行的全面实证实验也证明了 TAMMIE 的效率。详细的安全分析表明,我们提出的方案在比KBA 更强的攻击下是安全的。此外,对两个真实世界数据集和一个合成数据集进行的全面实证实验也证明了 TAMMIE 的效率。详细的安全分析表明,我们提出的方案在比KBA 更强的攻击下是安全的。此外,对两个真实世界数据集和一个合成数据集进行的全面实证实验也证明了 TAMMIE 的效率。
更新日期:2022-02-09
down
wechat
bug