Over-the-Air Collaborative Inference with Feature Differential Privacy

TL;DR

This paper introduces a privacy-preserving collaborative inference method for next-generation networks that protects sensitive features during transmission, reducing communication costs while maintaining inference accuracy.

Contribution

It proposes a novel over-the-air collaborative inference mechanism with feature differential privacy to enhance privacy and efficiency in AI applications.

Findings

Achieves privacy protection during feature transmission.

Reduces communication overhead significantly.

Maintains high inference accuracy.

Abstract

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.