With the advance in content-based image retrieval and the popularity of Data-as-a-Service, enterprises can outsource their image retrieval systems on cloud platforms to reduce heavy storage, computation, and communication burdens. However, this brings many privacy problems. Although several privacy-preserving image retrieval schemes have been proposed to protect users’ privacy, they have two major drawbacks: i) the outsourced images are fully encrypted and thus cannot be used for other applications, which makes them impractical; ii) they mainly focus on traditional image retrieval systems and do not use new techniques such as convolutional neural network (CNN) to improve the accuracy. To address the above problems, we propose a novel privacy-sensitive image retrieval scheme, named SensIR, to search for similar images from an outsourced image database. In particular, we propose a privacy region detection, PRDet, to prevent private regions of images from exposing. We also propose a partial CNN (PCNN) to reduce the impact of the encrypted pseudorandom pixels. Further, we use similarity-preserving hash encoding and propose a systematic methodology to improve the accuracy of PCNN-based image retrieval when the privacy regions are large. Extensive experiments are conducted to illustrate the efficiency of privacy protection and the superior of the proposed scheme.

随着基于内容的图像检索的发展以及“数据即服务”的普及，企业可以将其图像检索系统外包到云平台上，以减少繁重的存储，计算和通信负担。但是，这带来了许多隐私问题。尽管已经提出了几种保护隐私的图像检索方案来保护用户的隐私，但是它们有两个主要缺点：i）外包的图像是完全加密的，因此不能用于其他应用程序，这使它们不切实际；ii）他们主要关注传统的图像检索系统，不使用卷积神经网络（CNN）等新技术来提高准确性。为解决上述问题，我们提出了一种新的隐私敏感图像检索方案，名为SensIR，从外包图像数据库中搜索相似图像。特别是，我们提出了一种隐私区域检测PRDet，以防止图像的私有区域暴露出来。我们还提出了部分CNN（PCNN），以减少加密的伪随机像素的影响。此外，当隐私区域较大时，我们使用保留相似性的哈希编码并提出一种系统的方法来提高基于PCNN的图像检索的准确性。进行了广泛的实验，以说明隐私保护的效率和所提出方案的优越性。我们使用保留相似性的哈希编码，并提出一种系统的方法来提高隐私区域较大时基于PCNN的图像检索的准确性。进行了广泛的实验，以说明隐私保护的效率和所提出方案的优越性。我们使用保留相似性的哈希编码，并提出一种系统的方法，以在隐私区域较大时提高基于PCNN的图像检索的准确性。进行了广泛的实验，以说明隐私保护的效率和所提出方案的优越性。