Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing. Issue 128 (May 2023)

• Record Type: Journal Article
• Authors: Wang, Yu; Chen, Liquan; Wu, Ge; Yu, Kunliang; Lu, Tianyu
• Is Part Of: Computers & security. Issue 128(2023)
• Publication Date: 2023-05
• Subjects: Privacy preservation -- Content-based image retrieval (CBIR) -- Neural networks -- Approximate homomorphic encryption -- Deep neural networks -- Chaotic image encryption; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2023.103163
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: Use CKKS and chaotic encryption to protect users' privacy and original data. Our scheme supports floating point machine learning algorithms on the ciphertext. The ciphertext retrieval system supports large-scale image sets. Two kinds of neural networks to reduce multiplication times are proposed. Abstract: Smart devices offer a variety of more convenient forms to help us record our lives and generate a large amount of data in this process. Limited by the local storage capacity, many users outsource their image data directly to the cloud server. However, images stored in plaintext on the cloud server are very insecure, resulting in image privacy information can be easily leaked. Therefore, users will encrypt the images and outsource them to the cloud server, but the encrypted images cannot be retrieved. Therefore, we proposed a secure and efficient ciphertext image retrieval scheme based on image content retrieval (CBIR) and approximate homomorphic encryption (HE). First, we used approximate homomorphic encryption to encrypt images after resizing and uploaded the ciphertext images to the cloud for feature extraction of ciphertext. At the same time, the large images (size, dimension, and resolution) would generate data inflation after using homomorphic encryption. Therefore, the original images are encrypted using the chaotic image encryption scheme to reduce ciphertext size and computation costs. Second, we proposed two deepening network depth optimization strategies that address the problem of insufficient neural network depth. Finally, reducing the dimensionality of the ciphertext feature vector using locally sensitive hashing (LSH) can accelerate the retrieval of ciphertext images. Compared with other literature, our ciphertext image retrieval scheme can significantly reduce the rounds of user-server communication.