4 citations · 9 across the 6 of their papers we have counts for
9 papers
Secure Deep-JSCC Against Multiple Eavesdroppers
Seyyed Amirhossein Ameli Kalkhoran, Mehdi Letafati, Ecenaz Erdemir +3
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…
Generative Joint Source-Channel Coding for Semantic Image Transmission
Ecenaz Erdemir, Tze-Yang Tung, Pier Luigi Dragotti +1
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…
Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing
Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
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…
Privacy-Aware Communication Over a Wiretap Channel with Generative Networks
Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
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…
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary
Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
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 t…
Adversarial Robustness with Non-uniform Perturbations
Ecenaz Erdemir, Jeffrey Bickford, Luca Melis +1
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Pr…
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning
Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
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-Aware Location Sharing with Deep Reinforcement Learning
Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with unt…
Privacy-cost trade-off in a smart meter system with a renewable energy source and a rechargeable battery
Ecenaz Erdemir, Pier Luigi Dragotti, Deniz Gunduz
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 mu…