Abstract
With 5G and Internet technologies developing rapidly, outsourcing images to cloud servers has attracted growing attention. In existing technologies, images are often outsourced to cloud servers to reduce storage and computing burdens. However, outsourcing images to cloud servers without any processing may reveal the users’ privacy, because the images may contain sensitive information about users, such as faces and locations, especially in electronic investigation. To overcome the security problems in image retrieval, we propose a privacy-preserving image retrieval scheme based on deep convolutional neural network (DCNN) and vector homomorphic encryption (VHE). We adopt DCNN and hash algorithms to extract image feature vectors, which improves retrieval accuracy. By combining VHE and K-means outsourcing clustering algorithms, the cloud server can build encrypted index trees, which speeds up the search and reduces the computational cost. In addition, a lightweight access control technique is used to allow image owners to set access policies for datasets flexibly. We prove the security of the proposed scheme and show the effectiveness of the scheme through experiments. Our scheme is suitable for application in electronic image investigation systems (EIIs) to optimize the storage and search of police data.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of Shandong Province under Grant ZR2020MF056, in part by Henan Key Laboratory of Network Cryptography Technology (LNCT2021-A12), in part by the National Natural Science Foundation of China under Grant 62071280, and in part by the Major Scientific and Technological Innovation Project of Shandong Province under Grant 2020CXGC010115.
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Li, S., Wu, L., Meng, W. et al. DVPPIR: privacy-preserving image retrieval based on DCNN and VHE. Neural Comput & Applic 34, 14355–14371 (2022). https://doi.org/10.1007/s00521-022-07286-2
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DOI: https://doi.org/10.1007/s00521-022-07286-2