Enabling Simultaneous Content Regulation and Privacy Protection for Cloud Storage Image

The population of cloud computing greatly facilitates the sharing of explosively generated image today. While benefiting from the convenient of cloud, the privacy protection mechanism that commonly applied in cloud service makes the spreading of illegal and harmful data very hard to be detected or controlled. Such a realistic threat should be seriously treated, yet is largely overlooked in the literature. To address this issue, we propose the first cloud service framework that can simultaneously provide privacy protection and content regulation for the cloud storage image. In specific, we design a secure multi-party computation (MPC) protocol to protect the data privacy via random projection. By leveraging the distance preserving properties residing in random projection, we propose a privacy-preserving principal component analysis (PCA)-based recognition approach over the random projection domain to achieve content matching while respecting the data privacy. To facilitate the efficiency, we implement our system under the compressive sensing (CS) framework. Due to the compression effect of CS, the proposed cloud service can achieve remarkable reduction on the computation and communication complexity of the content matching process. Theoretical analysis and experimental results both show that our system can achieve privacy assurance and acceptable recognition performance, while with high efficiency.