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Found 8 papers, 4 papers with code
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education.
Private Graph Extraction via Feature Explanations
Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks.
Releasing Graph Neural Networks with Differential Privacy Guarantees
With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of trained GNNs.
Achieving differential privacy for $k$-nearest neighbors based outlier detection by data partitioning
When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed.
A Review of Anonymization for Healthcare Data
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector.
Membership Inference Attack on Graph Neural Networks
While choosing the simplest possible attack model that utilizes the posteriors of the trained model (black-box access), we thoroughly analyze the properties of GNNs and the datasets which dictate the differences in their robustness towards MI attack.
Context-aware Helpfulness Prediction for Online Product Reviews
In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review.
Human Activity Recognition for Mobile Robot
We trained and validated the model using the Vicon physical action dataset and also tested the model on our generated dataset (VMCUHK).
