Abstract
Along with the growing popularity of social networks, the number of multimedia image grows explosively. For the resource constrained owners, dealing with tremendous number of images on their own is a challenging job. Therefore, there is a general trend to outsource the heavy image processing (e.g., feature extraction) to the cloud. Abundant contents in images may expose the owner’s sensitive information (e.g., face, location and event), and outsourcing the image data to the untrusted cloud directly has raised privacy concerns of public. In this work, we explore the outsourcing of the famous feature extraction algorithm-Histogram of Oriented Gradients (HOG) to the public cloud with privacy protection. In our proposed scheme, the image owner encrypts the original images by using the Vector Homomorphic Encryption (VHE) that encrypt vector directly and is much suitable for image processing. Then the image owner sends the encrypted images to the cloud which elaborately applies the linear transformation of VHE to the realization of the improved HOG algorithm in ciphertext domain. The security analysis based on the hardness of Learning with Error (LWE) Problem verifies that the extraction of HOG features is privacy-preserving in our scheme without leaking privacy contents to any other parties. We implement pedestrian detection by using the extracted HOG features to validate the efficiency and effectiveness of our proposed scheme, and the result shows that our solution can extract the HOG features correctly in ciphertext domain and approximate the original HOG in plaintext domain. Compared with existing solution, our scheme has less time and communication cost of HOG feature extraction.
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Acknowledgement
This work is supported by the National Key Research and Development Program of China (2017YFB0802003, 2017YFB0802004) National Cryptography Development Fund during the 13th Five-year Plan Period (MMJJ20170216), the Fundamental Research Funds for the Central Universities (GK201702004), the National Natural Science Foundation of China under Grants U1633114 and China Postdoctoral Science Foundation funded project under Grant 2014M562309.
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Yang, H., Huang, Y., Yu, Y., Yao, M., Zhang, X. (2017). Privacy-Preserving Extraction of HOG Features Based on Integer Vector Homomorphic Encryption. In: Liu, J., Samarati, P. (eds) Information Security Practice and Experience. ISPEC 2017. Lecture Notes in Computer Science(), vol 10701. Springer, Cham. https://doi.org/10.1007/978-3-319-72359-4_6
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DOI: https://doi.org/10.1007/978-3-319-72359-4_6
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