Defenses to membership inference attacks: A survey
L Hu, A Yan, H Yan, J Li, T Huang, Y Zhang… - ACM Computing …, 2023 - dl.acm.org
Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …
computer vision and natural language processing. However, ML models are vulnerable to …
Trusted AI in multiagent systems: An overview of privacy and security for distributed learning
Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …
as well as the increasing concerns about sharing private data, there has been considerable …
Demystifying uneven vulnerability of link stealing attacks against graph neural networks
While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …
real-world applications, they have been shown to be vulnerable to a growing number of …
A review of federated learning: taxonomy, privacy and future directions
H Ratnayake, L Chen, X Ding - Journal of Intelligent Information Systems, 2023 - Springer
The data generated and stored in mobile devices owned by individuals as well as in various
organizations contains a large amount of valuable and important information that can be …
organizations contains a large amount of valuable and important information that can be …
Pre-trained perceptual features improve differentially private image generation
F Harder, MJ Asadabadi, DJ Sutherland… - arXiv preprint arXiv …, 2022 - arxiv.org
Training even moderately-sized generative models with differentially-private stochastic
gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of …
gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of …
How to combine membership-inference attacks on multiple updated machine learning models
A large body of research has shown that machine learning models are vulnerable to
membership inference (MI) attacks that violate the privacy of the participants in the training …
membership inference (MI) attacks that violate the privacy of the participants in the training …
Attribute inference attacks in online multiplayer video games: A case study on Dota2
Did you know that over 70 million of Dota2 players have their in-game data freely
accessible? What if such data is used in malicious ways? This paper is the first to investigate …
accessible? What if such data is used in malicious ways? This paper is the first to investigate …
[HTML][HTML] A survey on membership inference attacks and defenses in Machine Learning
Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning
Machine unlearning has become a promising solution for fulfilling the" right to be forgotten",
under which individuals can request the deletion of their data from machine learning …
under which individuals can request the deletion of their data from machine learning …
Fair privacy: how college students perceive fair privacy protection in online datasets
Y Tao, WH Wang - Information, Communication & Society, 2023 - Taylor & Francis
With the wide use of social media and other online services, people are getting more
concerned about online privacy. Social media platforms and other online companies are …
concerned about online privacy. Social media platforms and other online companies are …