论文标题
Primask：移动云推断的级联和勾结 - 有弹性数据掩盖
PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference
论文作者
论文摘要
移动云卸载对于基于大规模深模型的推理任务是必不可少的。但是，将隐私的推理数据传输到云引起关注的问题。本文介绍了一个称为Primask的系统的设计，其中移动设备使用一个名为MaskNet的秘密小型神经网络在传输前掩盖了数据。 Primask大大削弱了云恢复数据或提取某些私人属性的能力。 MaskNet是EM级联的，因为移动设备可以无缝使用或无缝使用，而无需对云推理服务进行任何修改。此外，手机使用不同的口罩，因此云与某些手机之间的勾结不会削弱对其他手机的保护。我们设计了一种{\ em拆分对抗性学习}方法来训练一个神经网络，该神经网络在运行时快速（两秒钟内）快速生成新的MaskNet。我们将Primask应用于具有不同方式和复杂性的三个移动传感应用程序，即人类活动识别，城市环境人群和驾驶员行为识别。结果表明，Primask在所有三个应用程序中的有效性。
Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile device uses a secret small-scale neural network called MaskNet to mask the data before transmission. PriMask significantly weakens the cloud's capability to recover the data or extract certain private attributes. The MaskNet is em cascadable in that the mobile can opt in to or out of its use seamlessly without any modifications to the cloud's inference service. Moreover, the mobiles use different MaskNets, such that the collusion between the cloud and some mobiles does not weaken the protection for other mobiles. We devise a {\em split adversarial learning} method to train a neural network that generates a new MaskNet quickly (within two seconds) at run time. We apply PriMask to three mobile sensing applications with diverse modalities and complexities, i.e., human activity recognition, urban environment crowdsensing, and driver behavior recognition. Results show PriMask's effectiveness in all three applications.
