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of 20
pro vyhledávání: '"Erdemir, Ecenaz"'
Autor:
Kalkhoran, Seyyed Amirhossein Ameli, Letafati, Mehdi, Erdemir, Ecenaz, Khalaj, Babak Hossein, Behroozi, Hamid, Gündüz, Deniz
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 eavesdropper
Externí odkaz:
http://arxiv.org/abs/2308.02892
Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission. However, these methods mostly focus on the distortion of the reconst
Externí odkaz:
http://arxiv.org/abs/2211.13772
Internet of things (IoT) devices, such as smart meters, smart speakers and activity monitors, have become highly popular thanks to the services they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine
Externí odkaz:
http://arxiv.org/abs/2202.05833
We study privacy-aware communication over a wiretap channel using end-to-end learning. Alice wants to transmit a source signal to Bob over a binary symmetric channel, while passive eavesdropper Eve tries to infer some sensitive attribute of Alice's s
Externí odkaz:
http://arxiv.org/abs/2110.04094
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Prior work mainly focus on crafting adversarial examples (AEs) with smal
Externí odkaz:
http://arxiv.org/abs/2102.12002
We consider a user releasing her data containing some personal information in return of a service. We model user's personal information as two correlated random variables, one of them, called the secret variable, is to be kept private, while the othe
Externí odkaz:
http://arxiv.org/abs/2102.08308
Internet of things (IoT) devices are becoming increasingly popular thanks to many new services and applications they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data w
Externí odkaz:
http://arxiv.org/abs/2003.02685
Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with untrusted third parties. In this work, we study privacy-utility trade-off
Externí odkaz:
http://arxiv.org/abs/1907.07606
We study the privacy-cost trade-off in a smart meter (SM) system with a renewable energy source (RES) and a finite-capacity rechargeable battery (RB). Privacy is measured by the mutual information rate between the energy demand and the energy receive
Externí odkaz:
http://arxiv.org/abs/1902.07739
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