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Found 10 papers, 2 papers with code
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size.
MIMO-GAN: Generative MIMO Channel Modeling
We propose generative channel modeling to learn statistical channel models from channel input-output measurements.
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries
Our work consists of two sides: We introduce sampling attack, a novel membership inference technique that unlike other standard membership adversaries is able to work under severe restriction of no access to scores of the victim model.
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains.
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e. g., online harassment, tracking).
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
We find such passive defenses ineffective against DNN stealing attacks.
Knockoff Nets: Stealing Functionality of Black-Box Models
We formulate model functionality stealing as a two-step approach: (i) querying a set of input images to the blackbox model to obtain predictions; and (ii) training a "knockoff" with queried image-prediction pairs.
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
At the core of FL is a network of anonymous user devices sharing training information (model parameter updates) computed locally on personal data.
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Images convey a broad spectrum of personal information.
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images
Third, we propose models that predict user specific privacy score from images in order to enforce the users' privacy preferences.
