Adversary for social good: Leveraging attribute-obfuscating attack to protect user privacy on social networks
As social networks become indispensable for people's daily lives, inference attacks pose
significant threat to users' privacy where attackers can infiltrate users' information and infer …
significant threat to users' privacy where attackers can infiltrate users' information and infer …
Does black-box attribute inference attacks on graph neural networks constitute privacy risk?
IE Olatunji, A Hizber, O Sihlovec, M Khosla - arXiv preprint arXiv …, 2023 - arxiv.org
Graph neural networks (GNNs) have shown promising results on real-life datasets and
applications, including healthcare, finance, and education. However, recent studies have …
applications, including healthcare, finance, and education. However, recent studies have …
Turning attacks into protection: Social media privacy protection using adversarial attacks
Abstract Machine learning, especially deep learning, has emerged as one of the most
powerful tools for attribute inference attacks over social media, which poses serious threats …
powerful tools for attribute inference attacks over social media, which poses serious threats …
Learning privacy-preserving graph convolutional network with partially observed sensitive attributes
Recent studies have shown Graph Neural Networks (GNNs) are extremely vulnerable to
attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs …
attribute inference attacks. To tackle this challenge, existing privacy-preserving GNNs …
Netfense: Adversarial defenses against privacy attacks on neural networks for graph data
IC Hsieh, CT Li - IEEE Transactions on Knowledge and Data …, 2021 - ieeexplore.ieee.org
Recent advances in protecting node privacy on graph data and attacking graph neural
networks (GNNs) gain much attention. The eye does not bring these two essential tasks …
networks (GNNs) gain much attention. The eye does not bring these two essential tasks …
Adversary for social good: Leveraging adversarial attacks to protect personal attribute privacy
Social media has drastically reshaped the world that allows billions of people to engage in
such interactive environments to conveniently create and share content with the public …
such interactive environments to conveniently create and share content with the public …
Adversarial privacy-preserving graph embedding against inference attack
Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media,
etc., has opened up a large source for graph data. Graph embedding has been proved …
etc., has opened up a large source for graph data. Graph embedding has been proved …
Devil in Disguise: Breaching Graph Neural Networks Privacy through Infiltration
Graph neural networks (GNNs) have been developed to mine useful information from graph
data of various applications, eg, healthcare, fraud detection, and social recommendation …
data of various applications, eg, healthcare, fraud detection, and social recommendation …
A unified framework of graph information bottleneck for robustness and membership privacy
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured
data. However, recent works show that GNNs are vulnerable to adversarial attacks which …
data. However, recent works show that GNNs are vulnerable to adversarial attacks which …
Maui: Black-Box Edge Privacy Attack on Graph Neural Networks
Graphs are ubiquitous data structures with nodes representing objects and edges
representing relationships between them. Graph Neural Networks (GNNs) have recently …
representing relationships between them. Graph Neural Networks (GNNs) have recently …