Multilevel Privacy Protection for Social Media Based on 2-D Compressive Sensing

Currently, the popularity of social networks has brought us rich and colorful displays and pleasant experiences. However, social networking is also a double-edged sword. While pleasing us, it also raises the issue of privacy disclosure of social media images. How to protect the privacy of media images has become a significant issue for social networks. When we post large quantities of images on social networks to share our daily lives, sometimes we only want to share them with specific friends or want friends with different permissions to see different image content, which involves hierarchical privacy protection. In particular, when the image to be shared contains multiple privacy-sensitive areas of different levels, and we only want to protect the privacy-sensitive areas rather than the whole image, how to protect each privacy-sensitive area is a major problem. Aiming at the above problems, a multilevel privacy protection scheme for image sharing in social networks based on 2-D compressive sensing is proposed. This scheme has the advantages of compressive sampling, privacy protection and controllable access. In addition, we propose a 2-D projected gradient algorithm with accompanying privacy region decryption (2DPG-APRD) for implementing the hierarchical privacy-preserving function of the proposed scheme. Experimental results show that our scheme has multilevel reconstruction quality, high-security intensity for different authorized users, and can well protect the privacy information of images. Therefore, the proposed scheme suits for many practical multilevel encryption situations.