Privacy-preserving multimedia cloud computing via compressive sensing and sparse representation

Cloud computing is an emerging technology developed for providing various computing and storage services over the Internet. In this paper, we proposed a privacy-preserving cloud-aware scenario for compressive multimedia applications, including multimedia compression, adaptation, editing/manipulation, enhancement, retrieval, and recognition. In the proposed framework, we investigate the applicability of our/existing compressive sensing (CS)-based multimedia compression and securely compressive multimedia 'trans-sensing' techniques based on sparse coding for securely delivering compressively sensed multimedia data over a cloud-aware scenario. Moreover, we also investigate the applicability of our/existing sparse coding-based frameworks for several multimedia applications by leveraging the strong capability of a media cloud. More specifically, to consider several fundamental challenges for multimedia cloud computing, such as security and network/device heterogeneities, we investigate the applications of CS and sparse coding techniques in multimedia delivery and applications. As a result, we can build a unified cloud-aware framework for privacy-preserving multimedia applications via sparse coding.