Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. Transmission of the extracted features entails the potential risk of exposing sensitive personal data. To address this issue, in this work a new privacy-protecting collaborative inference mechanism is developed. Under this mechanism, each edge device in the network protects the privacy of extracted features before transmitting them to a central server for inference. This mechanism aims to achieve two main objectives while ensuring effective inference performance: 1) reducing communication overhead, and 2) maintaining strict privacy guarantees during features transmission.

翻译：下一代网络中的协作推理能够增强人工智能（AI）应用，包括自动驾驶、个人识别和活动分类。该方法包含三个阶段：a) 通过传感进行数据采集，b) 特征提取，以及c) 用于传输的特征编码。传输提取的特征存在暴露敏感个人数据的潜在风险。为解决这一问题，本研究开发了一种新的隐私保护协作推理机制。在该机制下，网络中的每个边缘设备在将提取的特征传输至中央服务器进行推理之前，会先对这些特征进行隐私保护。该机制旨在确保有效推理性能的同时，实现两个主要目标：1) 降低通信开销，以及2) 在特征传输过程中保持严格的隐私保证。