Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Shen, Meng; Cheng, Guohua; Zhu, Liehuang; Du, Xiaojiang; Hu, Jiankun

doi:10.1016/j.future.2018.04.089

Cited by 81 publications

(17 citation statements)

References 27 publications

(52 reference statements)

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“…Qin et al [20] proposed an encrypted image retrieval approach in the cloud computing environment, which employs the improved Harris algorithm and Local Sensitive Hash (LSH) to retrieve encrypted images. Shen et al [21] proposed a secure content-based image retrieval method, which uses a secure multiparty computation technique to encrypt image features. Cheng et al [4] proposed an encrypted JPEG image retrieval scheme based on the Markov process, which uses encryption to encrypt DCT coefficients to protect the confidentiality of the JPEG image content.…”

Section: Related Workmentioning

confidence: 99%

Improved CNN-Based Hashing for Encrypted Image Retrieval

Pan

Wang

Qin

et al. 2021

Security and Communication Networks

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As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.

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Section: Related Workmentioning

confidence: 99%

Improved CNN-Based Hashing for Encrypted Image Retrieval

Pan

Wang

Qin

et al. 2021

Security and Communication Networks

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“…Plenty of approaches [12][13][14] have been proposed to enhance image privacy while preserving image operation privileges. These approaches focus on search efficiency optimization and image security-enhancing, but they are limited to a single centralized server strategy so that they cannot solve the problem of the server outage.…”

Section: Introductionmentioning

confidence: 99%

Privacy‐preserving image retrieval in a distributed environment

Zhou

Qin

Hou

et al. 2022

Int J of Intelligent Sys

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Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utilitysecurity dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be

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“…In addition, decryption processing within the cloud will reveal original images to unauthorized parties, such as cloud provider companies. Consequently, data processing based on encrypted information is promising and has become critical [15][16][17][18][19][20][21][22][23][24]. Image data are typically large, and image encryption with methods, such as data encryption standard (DES) or advanced encryption standard (AES), may be timeconsuming and unsuitable for low-computing devices, with limited power and computation capability, in IoT systems.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

Jeong

Moon

2021

IEEE Access

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With the emergence of cloud computing, large amounts of private data are stored and processed in the cloud. On the other hand, data owners (users) may not want to reveal data information to cloud providers to protect their privacy. Therefore, users may upload encrypted data to the cloud or thirdparty platforms, such as Google Cloud, Amazon Web Service, and Microsoft Azure. Conventionally, data must be decrypted before being analyzed in the cloud, which raises privacy concerns. Moreover, decryption of big data such as images requires enormous computation resources, which is unsuitable for energyconstrained devices, particularly Internet of Things (IoT) devices. Data privacy in most popular applications, such as image query or classification, can be preserved if encrypted images can be directly classified on the cloud or IoT devices without decryption. This paper proposes a high-speed double random phase encoding (DRPE) technique of encrypting images into white-noise images. DRPE-encrypted images are then uploaded and stored in the cloud. Images that are encrypted without being decrypted are classified using deep convolutional neural networks in the cloud. The simulation results indicated the feasibility and good performance of the proposed approach. The proposed privacy-preserving image classification method can be useful in data-sensitive fields, such as medicine and transportation.

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Content-based multi-source encrypted image retrieval in clouds with privacy preservation

Cited by 81 publications

References 27 publications

Improved CNN-Based Hashing for Encrypted Image Retrieval

Improved CNN-Based Hashing for Encrypted Image Retrieval

Privacy‐preserving image retrieval in a distributed environment

Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

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