# Adversary-Aware Private Inference over Wireless Channels

arXiv: https://arxiv.org/abs/2510.20518 · HTML: https://mlsanity.com/paper/2510.20518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Empirical Analysis of the Hessian of Over-Parametrized Neural Networks](https://mlsanity.com/paper/1706.04454/) (2017)
- [Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning](https://mlsanity.com/paper/1810.01075/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Robust Large Margin Deep Neural Networks](https://mlsanity.com/paper/1605.08254/) (2016)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [Collaborative Inference for AI-Empowered IoT Devices](https://mlsanity.com/paper/2207.11664/) (2022)
- [Over-the-Air Ensemble Inference with Model Privacy](https://mlsanity.com/paper/2202.03129/) (2022)
- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [Private Collaborative Edge Inference via Over-the-Air Computation](https://mlsanity.com/paper/2407.21151/) (2024)
- [LiveMap: Real-Time Dynamic Map in Automotive Edge Computing](https://mlsanity.com/paper/2012.10252/) (2020)
- [On the View-and-Channel Aggregation Gain in Integrated Sensing and Edge AI](https://mlsanity.com/paper/2311.07986/) (2023)
- [Collaborative Inference over Wireless Channels with Feature Differential Privacy](https://mlsanity.com/paper/2410.19917/) (2024)

## Abstract (arXiv)

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.