Preserving privacy and security in federated learning
Federated learning is known to be vulnerable to both security and privacy issues. Existing
research has focused either on preventing poisoning attacks from users or on concealing …
research has focused either on preventing poisoning attacks from users or on concealing …
Privacy-preserving explainable AI: a survey
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
A survey of privacy-preserving model explanations: Privacy risks, attacks, and countermeasures
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …
The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods
Since the advent of deep learning (DL), the field has witnessed a continuous stream of
innovations. However, the translation of these advancements into practical applications has …
innovations. However, the translation of these advancements into practical applications has …
SoK: Taming the Triangle--On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Machine learning techniques are increasingly used for high-stakes decision-making, such
as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure …
as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure …
Towards a game-theoretic understanding of explanation-based membership inference attacks
Abstract Model explanations improve the transparency of black-box machine learning (ML)
models and their decisions; however, they can also enable privacy threats like membership …
models and their decisions; however, they can also enable privacy threats like membership …
Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process
BX Collins, JC Bélisle-Pipon, BJ Evans… - JAMIA …, 2024 - academic.oup.com
Objectives Artificial intelligence (AI) proceeds through an iterative and evaluative process of
development, use, and refinement which may be characterized as a lifecycle. Within this …
development, use, and refinement which may be characterized as a lifecycle. Within this …
Privacy-Preserving Algorithmic Recourse
When individuals are subject to adverse outcomes from machine learning models, providing
a recourse path to help achieve a positive outcome is desirable. Recent work has shown …
a recourse path to help achieve a positive outcome is desirable. Recent work has shown …
Recent Advances in Federated Graph Learning
Abstract Graph Neural Networks (GNNs) exhibit tremendous potential in addressing graph-
related tasks such as node classification and link prediction. However, training GNNs on …
related tasks such as node classification and link prediction. However, training GNNs on …
XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution
Despite its significant benefits in enhancing the transparency and trustworthiness of artificial
intelligence (AI) systems, explainable AI (XAI) has yet to reach its full potential in real-world …
intelligence (AI) systems, explainable AI (XAI) has yet to reach its full potential in real-world …