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Found 9 papers, 6 papers with code
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server.
XRand: Differentially Private Defense against Explanation-Guided Attacks
Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.
User-Entity Differential Privacy in Learning Natural Language Models
In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs).
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning
In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss.
How to Backdoor HyperNetwork in Personalized Federated Learning?
This paper explores previously unknown backdoor risks in HyperNet-based personalized federated learning (HyperNetFL) through poisoning attacks.
Continual Learning with Differential Privacy
In this paper, we focus on preserving differential privacy (DP) in continual learning (CL), in which we train ML models to learn a sequence of new tasks while memorizing previous tasks.
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning
In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).
Ontology-based Interpretable Machine Learning for Textual Data
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models.
