Content-based image retrieval (CBIR) retrieves desired digital images from large databases. “Content-based” means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with images. It has different applications in different domains such as crime prevention, intellectual property, medical diagnosis, and face finding. Based on the aforementioned applications, there is a desideratum to retrieve users who have content-wise similar images, e.g., patients to speedup the diagnosis process or individuals to identify an unidentified criminals or group individuals with similar interests. Most image owners are outsourcing their images to the cloud because of low storage and computation costs. Although existing encryption mechanisms protect images from unauthorized access, these techniques increase the computational complexity of executing arbitrary functions on the outsourced images. In this paper, our focus is to efficiently and privately identify users who have content-wise similar encrypted images stored in the cloud. The proposed scheme utilizes the Intel Software Guard Extensions (Intel SGX) architecture, the Convolutional Neural Network (CNN), and Locality Sensitive Hashing (LSH) for minhash signatures. Considering symmetric encryption, we experimentally show that the proposed approach is only five times slower than the plaintext approach with just one round of interaction with the cloud server.

基于内容的图像检索（CBIR）从大型数据库中检索所需的数字图像。“基于内容”是指搜索分析图像的内容，而不是分析与图像关联的元数据（例如关键字，标签或描述）。它在预防犯罪，知识产权，医疗诊断和面部识别等不同领域具有不同的应用。基于前述的应用，存在着对具有内容方面相似的图像的用户（例如，患者以加快诊断过程的速度）或个人以识别身份不明的罪犯或具有相似兴趣的个人进行检索的需求。由于较低的存储和计算成本，大多数图像所有者将其图像外包给云。尽管现有的加密机制可以防止未经授权的图像访问，这些技术增加了在外包图像上执行任意功能的计算复杂性。在本文中，我们的重点是有效和私密地标识在云中存储有内容方面相似的加密图像的用户。拟议的方案利用了Intel软件保护扩展（Intel SGX）架构，卷积神经网络（CNN）和本地敏感哈希（LSH）来实现minhash签名。考虑到对称加密，我们通过实验表明，与仅与云服务器进行一轮交互的纯文本方法相比，该方法仅慢五倍。我们的重点是有效和私密地标识在云中存储有内容方面相似的加密图像的用户。拟议的方案利用了Intel软件保护扩展（Intel SGX）架构，卷积神经网络（CNN）和本地敏感哈希（LSH）来实现minhash签名。考虑到对称加密，我们通过实验证明了该方法仅比与纯文本方法慢五倍，而与云服务器仅进行了一轮交互。我们的重点是有效和私密地标识在云中存储有内容方面相似的加密图像的用户。拟议的方案利用了Intel软件保护扩展（Intel SGX）架构，卷积神经网络（CNN）和本地敏感哈希（LSH）来实现minhash签名。考虑到对称加密，我们通过实验表明，与仅与云服务器进行一轮交互的纯文本方法相比，该方法仅慢五倍。