保护无线边缘设备的隐私，防止特征被窃取。
Adversary-Aware Private Inference over Wireless Channels
- 在传输前对感知特征进行变换，防止隐私泄露。
- 针对个体特征提出新型隐私保护框架。
- 适合自动驾驶、环境监测等敏感场景使用。
基于AI的无线边缘感知有望显著提升人工智能应用，特别是在自动驾驶和环境监测等视觉与感知任务中。AI系统依赖高效的模型学习与推理。在推理阶段，从传感数据中提取的特征用于预测任务（如分类或回归）。在边缘网络中，传感器与模型服务器通常不共处一地，需传输特征数据。由于敏感个人数据可能被攻击者重构，必须对特征进行转换以降低隐私泄露风险。尽管差分隐私机制可保护有限数据集，但对单个特征的保护尚未解决。本文提出一种新型隐私保护框架，使设备在将提取的特征传至模型服务器前，先对其进行变换。
原文摘要 · Abstract (English)
AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for vision and perception tasks such as in autonomous driving and environmental monitoring. AI systems rely both on efficient model learning and inference. In the inference phase, features extracted from sensing data are utilized for prediction tasks (e.g., classification or regression). In edge networks, sensors and model servers are often not co-located, which requires communication of features. As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations. While differential privacy mechanisms provide a means of protecting finite datasets, protection of individual features has not been addressed. In this paper, we propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
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