Privacy-Preserving Multimedia Mobile Cloud Computing Using Cost-Effective Protective Perturbation

Mobile cloud computing has been adopted in many multimedia applications, where resource-constrained mobile devices send multimedia data (e.g., images) to remote cloud servers to request computation intensive multimedia services (e.g., image recognition). Despite the performance improvement, the cloud-based mechanism often causes privacy concerns as the user data is offloaded to untrusted cloud servers. Existing solutions require computation-intensive perturbation generation on resource-constrained mobile devices. Also, the protected images are not compliant with standard image compression algorithms, leading to significant bandwidth consumption. We develop a novel privacy-preserving multimedia mobile cloud computing framework, namely PMC2, to address the resource and bandwidth challenges. PMC2 employs confidential computing on an edge server to deploy the perturbation generator, which addresses the on-device resource challenge. Also, we develop a neural compressor for the protected images to address the bandwidth challenge. Our evaluations of PMC2 demonstrate superior latency, power efficiency, and bandwidth consumption while maintaining high accuracy in the target multimedia service.