arXiv 2409.017102024-09-03cs.MM

使用保护性扰动的隐私保护多媒体移动云计算

Privacy-Preserving Multimedia Mobile Cloud Computing Using Protective Perturbation

Zhongze Tang, Mengmei Ye, Yao Liu, Sheng Wei

AI总结：

本文提出隐私保护多媒体移动云计算框架 $PMC^2$，通过在云端安全部署扰动生成器和训练专用神经压缩器，解决移动设备资源受限和带宽消耗问题，同时保持多媒体服务准确性。

摘要：

移动云计算已被许多多媒体应用采用，其中资源受限的移动设备将多媒体数据（例如图像）发送到远程云服务器，以请求计算密集型多媒体服务（例如图像识别）。虽然基于云的机制显著提升了移动应用的性能，但由于多媒体数据和服务从可信的用户设备卸载到不可信的云服务器，这种机制常常引发隐私问题。最近的一些研究提出了基于扰动的隐私保护机制，通过对卸载的多媒体数据进行混淆，在不影响远程多媒体服务功能的前提下消除隐私暴露。然而，现有的隐私保护方法需要在资源受限的移动设备上部署计算密集型的扰动生成过程。此外，混淆后的图像通常不符合标准图像压缩算法的要求，并且会消耗大量带宽。在本文中，我们开发了一种新颖的隐私保护多媒体移动云计算框架，即 $PMC^2$，以应对资源和带宽方面的挑战。$PMC^2$ 在云中采用安全机密计算来部署扰动生成器，从而在维护隐私的同时解决资源挑战。此外，我们开发了一种神经压缩器，专门训练用于压缩扰动图像，以解决带宽挑战。我们在端到端移动云计算系统中实现了 $PMC^2$，基于该系统的评估表明，$PMC^2$ 在保持目标多媒体服务高准确率的同时，实现了优越的延迟、功耗效率和带宽消耗表现。

英文摘要：

Mobile cloud computing has been adopted in many multimedia applications, where the resource-constrained mobile device sends multimedia data (e.g., images) to remote cloud servers to request computation-intensive multimedia services (e.g., image recognition). While significantly improving the performance of the mobile applications, the cloud-based mechanism often causes privacy concerns as the multimedia data and services are offloaded from the trusted user device to untrusted cloud servers. Several recent studies have proposed perturbation-based privacy preserving mechanisms, which obfuscate the offloaded multimedia data to eliminate privacy exposures without affecting the functionality of the remote multimedia services. However, the existing privacy protection approaches require the deployment of computation-intensive perturbation generation on the resource-constrained mobile devices. Also, the obfuscated images are typically not compliant with the standard image compression algorithms and suffer from significant bandwidth consumption. In this paper, we develop a novel privacy-preserving multimedia mobile cloud computing framework, namely $PMC^2$, to address the resource and bandwidth challenges. $PMC^2$ employs secure confidential computing in the cloud to deploy the perturbation generator, which addresses the resource challenge while maintaining the privacy. Furthermore, we develop a neural compressor specifically trained to compress the perturbed images in order to address the bandwidth challenge. We implement $PMC^2$ in an end-to-end mobile cloud computing system, based on which our evaluations demonstrate superior latency, power efficiency, and bandwidth consumption achieved by $PMC^2$ while maintaining high accuracy in the target multimedia service.

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