Edge intelligence (EI), integrated with the merits of both edge computing and artificial intelligence, has been proposed recently to realize intensive computation and low delay inference in the edge of the Internet of Things (IoT). However, the constrained energy and computation ability in edge devices become the main obstacle for EI application in IoT. There is a flexible multi-grade EI deployment scheme in which multiple machine models are provided to meet the different requirements of edge users (inference accuracy, inference delay, inference cost, etc.). In the multi-grade EI model, low-grade models contain information of high-grade ones, and the inference cost increases with the inference accuracy. Thus, some attackers or malicious users may want to obtain some private information of those high-grade models by querying low-grade ones at low cost. Specifically, in this article we study the vulnerability of the multi-grade EI model against membership inference attack (MIA). First, we propose an attack model for multi-grade EI. Second, we reveal different grades of EI on vulnerability by comparing inference accuracy and cost in different datasets. The experiment results show that in multi-grade EI, a low-grade model would leak privacy information of a high-grade model under MIA.

中文翻译：

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边缘智能中多级服务模型的成员推理攻击


边缘智能（EI）融合了边缘计算和人工智能的优点，最近被提出，旨在实现物联网（IoT）边缘的密集计算和低延迟推理。然而，边缘设备有限的能量和计算能力成为EI在物联网中应用的主要障碍。具有灵活的多级EI部署方案，提供多种机器模型，满足边缘用户的不同需求（推理精度、推理延迟、推理成本等）。在多级EI模型中，低级模型包含高级模型的信息，推理成本随着推理精度的提高而增加。因此，一些攻击者或恶意用户可能希望通过低成本查询低等级模型来获取这些高等级模型的一些隐私信息。具体来说，在本文中，我们研究了多级 EI 模型针对成员推理攻击 (MIA) 的脆弱性。首先，我们提出了多级 EI 的攻击模型。其次，我们通过比较不同数据集中的推理准确性和成本来揭示漏洞的不同 EI 等级。实验结果表明，在多等级EI中，低等级模型会在MIA下泄露高等级模型的隐私信息。