Integrated secure watermark detection and privacy preserving storage in the compressive sensing domain

Secure watermark detection techniques are important to protect the secrecy of the watermark pattern. In this paper, we identify an application scenario that requires performing secure watermark detection in the cloud, and in the meantime, supporting privacy preserving multimedia data storage using the cloud. We then propose a compressive sensing (CS) based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. The compressive sensing transformation is executed by a MPC protocol under the semi-honest security model to protect the privacy of the compressive sensing matrix and the watermark pattern. We derive the lower bound of the expected watermark detection performance in the compressive sensing domain, given the original image, watermark pattern and only the size of the compressive sensing matrix. The lower bound has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.