The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations.

中文翻译：

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PEACE：移动边缘计算的隐私保护和成本高效的任务卸载

移动设备有限的信息处理能力和电池寿命正成为向客户提供更先进和高质量服务的瓶颈。为了解决这个问题，最近提倡的移动边缘计算 (MEC) 架构很有前景，其基本思想是将计算资源带到网络边缘，并允许用户将需要资源的计算任务无线卸载到附近的 MEC 服务器上，以便潜在地更快的执行和更低的电池消耗。尽管如此，现有对 MEC 隐私方面的理解仍远未完成。在这项工作中，针对 MEC 确定了一种通过利用从用户卸载的特征任务来侵犯用户隐私的用户存在推理攻击。为其他应用程序开发的现有隐私保护技术无法应用于在 MEC 中击败这种攻击，因为它们可能会破坏最佳任务卸载调度并导致用户体验严重下降。考虑到这一点，基于通用 Lyapunov 优化框架的这项工作开发了一种新颖的隐私保护和成本效益 (PEACE) 任务卸载方案，该方案可以在保护用户隐私的同时仍确保最佳用户体验。通过分析和仿真验证了所提出方案的有效性。在这项基于通用 Lyapunov 优化框架的工作中，开发了一种新颖的隐私保护和成本效益 (PEACE) 任务卸载方案，该方案可以在保护用户隐私的同时仍确保最佳用户体验。通过分析和仿真验证了所提出方案的有效性。在这项基于通用 Lyapunov 优化框架的工作中，开发了一种新颖的隐私保护和成本效益 (PEACE) 任务卸载方案，该方案可以在保护用户隐私的同时仍确保最佳用户体验。通过分析和仿真验证了所提出方案的有效性。