IoT Privacy Protection: JPEG-TPE With Lower File Size Expansion and Lossless Decryption

Yuan Yuan, Hongjie He, Hadi Amirpour, Lingfeng Qu, Christian Timmerer, Fan Chen

Abstract: With the development of Internet of Things (IoT) and cloud services, many images generated from IoT devices are stored in the cloud, calling for efficient data encryption methods. To balance the security and usability, the thumbnail preserving encryption (TPE) has emerged. However, existing JPEG image-based TPE (JPEG-TPE) schemes face challenges in achieving low file extension, lossless decryption, and better privacy protect of detailed information. To solve these challenges, we propose a novel JPEG-TPE scheme. First, to achieve a smaller file size expansion and preserve the thumbnail, we reallocate the values, maintaining the sum for the DC difference instead of the DC coefficient. To ensure that the coefficients do not overflow, the valid range of reallocated difference is constrained not only by the sum but also by the neighborhood difference. Second, to preserve the file size of AC encryption while improving the security of detailed information, the AC coefficient groups with undivided RSV are permuted adaptively. Besides, the intra-TPE block swapping of DC difference, quantization table modification, nonzero AC coefficients mapping, and block permutation are used to further encrypt the image. The experimental results show that the proposed JPEG-TPE scheme achieves lossless decryption, reducing the file size expansion of encrypted images from 15.41% to 0.64% compared to the state-of-the-art scheme. Additionally, it is observed that the proposed method can effectively resist against various attacks, including the deep-learning-based super-resolution attack.