Secure Social Multimedia Big Data Sharing Using Scalable JFE in the TSHWT Domain

Abstract

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the meantime, social networking platforms based on cloud computing have made multimedia big data sharing in social networks easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use, such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social networks. In this article, a joint fingerprinting and encryption (JFE) scheme based on tree-structured Haar wavelet transform (TSHWT) is proposed with the purpose of protecting media distribution in social network environments. The motivation is to map hierarchical community structure of social networks into a tree structure of Haar wavelet transform for fingerprinting and encryption. First, fingerprint code is produced using social network analysis (SNA). Second, the content is decomposed based on the structure of fingerprint code by the TSHWT. Then, the content is fingerprinted and encrypted in the TSHWT domain. Finally, the encrypted contents are delivered to users via hybrid multicast-unicast. The proposed method, to the best of our knowledge, is the first scalable JFE method for fingerprinting and encryption in the TSHWT domain using SNA. The use of fingerprinting along with encryption using SNA not only provides a double layer of protection for social multimedia sharing in social network environment but also avoids big data superposition effect. Theory analysis and experimental results show the effectiveness of the proposed JFE scheme.