MU-TEIR: Traceable Encrypted Image Retrieval in the Multi-user Setting

The encrypted image retrieval technique allows users to retrieve images in an encrypted manner without decrypting images. However, most of the existing schemes still are vulnerable to security threats and inefficiency, caused by malicious users and inefficient feature extraction methods, respectively. To this end, we propose a traceable encrypted image retrieval in the multi-user setting in this paper, termed as MU-TEIR. First, MU-TEIR employs a convolutional neural network VGG16 to extract image feature vectors and calculate the mean and variance of the feature vectors to construct the index, then encrypts index with the distributed two trapdoors public-key cryptosystem. After that, MU-TEIR protects image content by encrypting each image pixel with a standard stream cipher. Furthermore, MU-TEIR utilizes a watermark-based mechanism to prevent malicious query users from maliciously distributing images. Detailed security analysis shows that MU-TEIR protects the outsourced images and indexes security as well as query privacy, and can track malicious users. Experimental results verify effectiveness of MU-TEIR.