Version 1
: Received: 19 July 2023 / Approved: 20 July 2023 / Online: 20 July 2023 (11:55:45 CEST)
Version 2
: Received: 19 September 2023 / Approved: 21 September 2023 / Online: 21 September 2023 (11:24:52 CEST)
How to cite:
Zhou, C.; Zhou, Y.; An, X.; Liu, Y.; Wang, M.; Liu, X. Verifiable Privacy Preservation Scheme for Outsourcing Medical Image to Cloud Through ROI Based Crypto-Watermarking. Preprints2023, 2023071435. https://doi.org/10.20944/preprints202307.1435.v2
Zhou, C.; Zhou, Y.; An, X.; Liu, Y.; Wang, M.; Liu, X. Verifiable Privacy Preservation Scheme for Outsourcing Medical Image to Cloud Through ROI Based Crypto-Watermarking. Preprints 2023, 2023071435. https://doi.org/10.20944/preprints202307.1435.v2
Zhou, C.; Zhou, Y.; An, X.; Liu, Y.; Wang, M.; Liu, X. Verifiable Privacy Preservation Scheme for Outsourcing Medical Image to Cloud Through ROI Based Crypto-Watermarking. Preprints2023, 2023071435. https://doi.org/10.20944/preprints202307.1435.v2
APA Style
Zhou, C., Zhou, Y., An, X., Liu, Y., Wang, M., & Liu, X. (2023). Verifiable Privacy Preservation Scheme for Outsourcing Medical Image to Cloud Through ROI Based Crypto-Watermarking. Preprints. https://doi.org/10.20944/preprints202307.1435.v2
Chicago/Turabian Style
Zhou, C., Min Wang and XiangZhi Liu. 2023 "Verifiable Privacy Preservation Scheme for Outsourcing Medical Image to Cloud Through ROI Based Crypto-Watermarking" Preprints. https://doi.org/10.20944/preprints202307.1435.v2
Abstract
A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.
Keywords
sharing secret; data outsourcing; reversible watermarking; chaotic map
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
