Adaptive and safe presentation strategy of image information on social platform
Huaibo Sun, Hong Luo, Tin‐Yu Wu, Mohammad S. Obaidat · 2017
With the development of social network, there are more and more people to share pictures on social platforms. Since the information contained in picture has the different requirements for confidentiality, it makes the selective presentation of secret information to be an urgent problem. Estimating user's privilege of gaining some regions based on his/her attributes is a novel solution. But there are few perfect solutions aiming at the strategy of adaptive calculation for user's privilege in the existing literatures, especially for the scenario in which the real values of some attributes have priorities. In this work, based on the cipher text-policy attribute-based encryption (CP-ABE), we propose an adaptive and safe presenting scheme for the information contained in a picture. This scheme firstly embeds the confidential data outside the secret region, and generates the image mosaic in the secret region; when someone requesting the original version of image, it adaptively calculates the recovery privilege of requestor with the strategy proposed in this paper, then precisely present some regions based on the privilege level of requestor. Moreover, we firstly propose the vote-attribute which facilitates the attribute revocation. The experiments demonstrate that, based on the privilege level, the proposed scheme can safely present the original version of the corresponding image region, and expediently achieve the attribute revocation. Compared with other algorithms, our scheme can restore the original version of image with only 1/2 secret data, and spend little time over the attribute revocation. Besides, the average of peak signal to noise ratio (PSNR) is 4dB more than the algorithms available, and the standard variance of PSNR is less than 0.4.