作者
Seyyed Amirhossein Ameli Kalkhoran, Mehdi Letafati, Ecenaz Erdemir, Babak Hossein Khalaj, Hamid Behroozi, Deniz Gündüz
发表日期
2023/8
期刊
arXiv e-prints
页码范围
arXiv: 2308.02892
简介
In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the …
学术搜索中的文章
S Amirhossein Ameli Kalkhoran, M Letafati, E Erdemir… - arXiv e-prints, 2023