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Found 8 papers, 1 papers with code
Secure Deep-JSCC Against Multiple Eavesdroppers
Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme.
Generative Joint Source-Channel Coding for Semantic Image Transmission
In GenerativeJSCC, we carry out end-to-end training of an encoder and a StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms DeepJSCC both in terms of distortion and perceptual quality.
Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing
For privacy measure, we consider both the probability of correctly detecting the true value of the secret and the mutual information (MI) between the secret and the released data.
Privacy-Aware Communication Over a Wiretap Channel with Generative Networks
We study privacy-aware communication over a wiretap channel using end-to-end learning.
Adversarial Robustness with Non-uniform Perturbations
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors.
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary
We consider a user releasing her data containing some personal information in return of a service.
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning
We measure the privacy leakage by the mutual information between the user's true data sequence and shared version.
Privacy-Aware Location Sharing with Deep Reinforcement Learning
Existing approaches are mainly focused on privacy of sharing a single location or myopic location trace privacy; neither of them taking into account the temporal correlations between the past and current locations.
Information Theory Cryptography and Security Information Theory
