DVPPIR: privacy-preserving image retrieval based on DCNN and VHE

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.

Automated summary

In this paper, a privacy-preserving image retrieval scheme is proposed, which combines deep convolutional neural network and vector homomorphic encryption to improve retrieval accuracy. By building encrypted index trees using VHE and K-means outsourcing clustering algorithms, the search process is accelerated and computational cost is reduced. Additionally, a lightweight access control technique is utilized for flexible dataset access policy settings.