# Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving

arXiv: https://arxiv.org/abs/2209.09008 · HTML: https://mlsanity.com/paper/2209.09008/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

## Built on by (most cited first, 10 of 10)

- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses](https://mlsanity.com/paper/2306.03528/) (2023)
- [VideoQA-SC: Adaptive Semantic Communication for Video Question Answering](https://mlsanity.com/paper/2406.18538/) (2024)
- [Building Semantic Communication System via Molecules: An End-to-End Training Approach](https://mlsanity.com/paper/2404.09595/) (2024)
- [Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks](https://mlsanity.com/paper/2409.15695/) (2024)
- [IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation](https://mlsanity.com/paper/2411.01821/) (2024)
- [Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks](https://mlsanity.com/paper/2412.01650/) (2024)
- [SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication](https://mlsanity.com/paper/2509.04803/) (2025)
- [Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations](https://mlsanity.com/paper/2512.24452/) (2025)
- [Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications](https://mlsanity.com/paper/2606.31973/) (2026)

## Abstract (arXiv)

Semantic communication is implemented based on shared background knowledge, but the sharing mechanism risks privacy leakage. In this letter, we propose an encrypted semantic communication system (ESCS) for privacy preserving, which combines universality and confidentiality. The universality is reflected in that all network modules of the proposed ESCS are trained based on a shared database, which is suitable for large-scale deployment in practical scenarios. Meanwhile, the confidentiality is achieved by symmetric encryption. Based on the adversarial training, we design an adversarial encryption training scheme to guarantee the accuracy of semantic communication in both encrypted and unencrypted modes. Experiment results show that the proposed ESCS with the adversarial encryption training scheme can perform well regardless of whether the semantic information is encrypted. It is difficult for the attacker to reconstruct the original semantic information from the eavesdropped message.