Transparent hashing in the encrypted domain for privacy preserving image retrieval

Search through a database of encrypted images against a crumpled and encrypted query will remain privacy preserving only if comparisons between selective features derived from these images is executed in the encrypted domain itself. To facilitate this, the encryption process must remain transparent to specific image statistics computed in the spatial or transform domain. Consequently, the perceptual hash formed by quantizing the image statistics remains the same before and after the encryption process. In this paper, we propose a transparent privacy preserving hashing scheme tailored to preserve the DCT-AC coefficient distributions, despite a constrained inter-block shuffling operation. These DCT distributions can be mapped onto a generalized Gaussian model characterized by shape and scale parameters, which can be quantized and Gray-coded into a binary hash matrix. The encryption scheme has been shown to be perceptually secure and does not impair the search reliability and accuracy of the hashing procedure. Experimental results have been provided to verify the robustness of the hash to content-preserving transformations, while demonstrating adequate sensitivity to discriminate between different images.

[1]  Soo-Chang Pei,et al.  Homomorphic encryption-based secure SIFT for privacy-preserving feature extraction , 2011, Electronic Imaging.

[2]  O. Chum,et al.  Geometric min-Hashing: Finding a (thick) needle in a haystack , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Vishal Monga,et al.  Perceptual Image Hashing Via Feature Points: Performance Evaluation and Tradeoffs , 2006, IEEE Transactions on Image Processing.

[4]  Alberto Leon-Garcia,et al.  Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[5]  Kannan Karthik,et al.  Authenticating encrypted data , 2011, 2011 National Conference on Communications (NCC).

[6]  Vishal Monga,et al.  A clustering based approach to perceptual image hashing , 2006, IEEE Transactions on Information Forensics and Security.

[7]  Yanqiang Lei,et al.  A Robust Content in DCT Domain for Image Authentication , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

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

[9]  Jiwu Huang,et al.  Random Gray code and its performance analysis for image hashing , 2011, Signal Process..

[10]  Dimitris Achlioptas,et al.  Database-friendly random projections , 2001, PODS.

[11]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Ramarathnam Venkatesan,et al.  Robust image hashing , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[13]  Claude E. Shannon,et al.  Communication theory of secrecy systems , 1949, Bell Syst. Tech. J..

[14]  Kannan Karthik Robust image hashing by downsampling: Between mean and median , 2011, 2011 World Congress on Information and Communication Technologies.

[15]  Min Wu,et al.  Secure image retrieval through feature protection , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Santosh S. Vempala,et al.  The Random Projection Method , 2005, DIMACS Series in Discrete Mathematics and Theoretical Computer Science.

[17]  Min Wu,et al.  Robust and secure image hashing , 2006, IEEE Transactions on Information Forensics and Security.

[18]  J. A. Domínguez-Molina A practical procedure to estimate the shape parameter in the generalized Gaussian distribution , 2002 .