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.

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