Toward Efficient Encrypted Image Retrieval in Cloud Environment

Outsourcing image search services to public clouds is an ever-increasing trend. However, directly outsourcing image datasets to untrusted clouds introduces privacy concerns. Several secure image retrieval schemes have been proposed recently. However, most of them require participation from image owners when building secure indexes, which wastes many computational resources of the image owners. Several schemes are proposed to solve this problem, but they suffer from low search accuracy on large datasets. In this paper, we propose the first secure image retrieval scheme that simultaneously solves these two problems. To obtain higher search accuracy, we extract image features via fine-tuned convolutional neural networks. Then, the image features are encrypted by using the secure k-Nearest Neighbor algorithm. To improve search speed and reduce the cost of image owners, we let cloud servers locally build a secure hierarchical index graph by using the encrypted image features. Besides, the secure index can be built and updated in parallel. We provide security analysis for the proposed scheme. Performance evaluations on the CIFAR-10 dataset show that the proposed scheme is practical. Moreover, compared with a recent scheme, our scheme can save more index construction time and cost of image owners when building secure indexes.

[1]  Victor S. Lempitsky,et al.  Neural Codes for Image Retrieval , 2014, ECCV.

[2]  Zheng Lin,et al.  Deep Supervised Hashing for Multi-Label and Large-Scale Image Retrieval , 2017, ICMR.

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[4]  Laurence T. Yang,et al.  Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning , 2016, IEEE Transactions on Computers.

[5]  Zhiguang Qin,et al.  MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing , 2019, IEEE Access.

[6]  Tieniu Tan,et al.  Deep semantic ranking based hashing for multi-label image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Shucheng Yu,et al.  SEISA: Secure and efficient encrypted image search with access control , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[8]  Hao Li,et al.  An Encrypted Image Retrieval Method Based on Harris Corner Optimization and LSH in Cloud Computing , 2019, IEEE Access.

[9]  Jennifer Seberry,et al.  A multistage protocol for aggregated queries in distributed cloud databases with privacy protection , 2019, Future Gener. Comput. Syst..

[10]  Wanlei Zhou,et al.  Novel Multi-Keyword Search on Encrypted Data in the Cloud , 2019, IEEE Access.

[11]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[12]  Nikos Mamoulis,et al.  Secure kNN computation on encrypted databases , 2009, SIGMOD Conference.

[13]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[14]  Xinpeng Zhang,et al.  Markov Process Based Retrieval for Encrypted JPEG Images , 2015, 2015 10th International Conference on Availability, Reliability and Security.

[15]  Xiaofeng Gu,et al.  A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks , 2017, IEEE Access.

[16]  Yu Bai,et al.  Surf feature extraction in encrypted domain , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[17]  Qi Tian,et al.  SIFT Meets CNN: A Decade Survey of Instance Retrieval , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Kakali Chatterjee,et al.  Cloud security issues and challenges: A survey , 2017, J. Netw. Comput. Appl..

[19]  Jen-Hao Hsiao,et al.  Deep learning of binary hash codes for fast image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[20]  Dawn Xiaodong Song,et al.  Practical techniques for searches on encrypted data , 2000, Proceeding 2000 IEEE Symposium on Security and Privacy. S&P 2000.

[21]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[22]  Vladimir Krylov,et al.  Scalable Distributed Algorithm for Approximate Nearest Neighbor Search Problem in High Dimensional General Metric Spaces , 2012, SISAP.

[23]  Vania V. Estrela,et al.  Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare , 2016, ArXiv.

[24]  Qian Wang,et al.  Searchable Encryption over Feature-Rich Data , 2018, IEEE Transactions on Dependable and Secure Computing.

[25]  Cong Wang,et al.  Hardening Distributed and Encrypted Keyword Search via Blockchain , 2017, 2017 IEEE Symposium on Privacy-Aware Computing (PAC).

[26]  Zhihua Xia,et al.  Secure Image LBP Feature Extraction in Cloud-Based Smart Campus , 2018, IEEE Access.

[27]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[28]  Hanjiang Lai,et al.  Supervised Hashing for Image Retrieval via Image Representation Learning , 2014, AAAI.

[29]  Naixue Xiong,et al.  EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing , 2017, Inf. Sci..

[30]  Xin Li,et al.  CASHEIRS: Cloud assisted scalable hierarchical encrypted based image retrieval system , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[31]  Zhihua Xia,et al.  A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment , 2019, Computers, Materials & Continua.

[32]  João Leitão,et al.  Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories , 2019, IEEE Transactions on Cloud Computing.

[33]  Vladimir Krylov,et al.  Approximate nearest neighbor algorithm based on navigable small world graphs , 2014, Inf. Syst..

[34]  Xinpeng Zhang,et al.  Encrypted JPEG image retrieval using block-wise feature comparison , 2016, J. Vis. Commun. Image Represent..

[35]  Ling Gao,et al.  Face Detection for Privacy Protected Images , 2019, IEEE Access.

[36]  Michael Naehrig,et al.  CryptoNets: applying neural networks to encrypted data with high throughput and accuracy , 2016, ICML 2016.

[37]  Yury A. Malkov,et al.  Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Rong Hao,et al.  Forward Secure Conjunctive-Keyword Searchable Encryption , 2019, IEEE Access.

[39]  Svetlana Lazebnik,et al.  Iterative quantization: A procrustean approach to learning binary codes , 2011, CVPR 2011.

[40]  Geoffrey E. Hinton,et al.  Using very deep autoencoders for content-based image retrieval , 2011, ESANN.

[41]  Qian Wang,et al.  DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection , 2019, IEEE Transactions on Image Processing.

[42]  Siu-Ming Yiu,et al.  A Privacy-Preserving Multi-Pattern Matching Scheme for Searching Strings in Cloud Database , 2017, 2017 15th Annual Conference on Privacy, Security and Trust (PST).

[43]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[44]  Soo-Chang Pei,et al.  Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT , 2012, IEEE Transactions on Image Processing.

[45]  Min Wu,et al.  Enabling search over encrypted multimedia databases , 2009, Electronic Imaging.