5 citations · 9 across the 7 of their papers we have counts for
8 papers
LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization
Huy Phan, Kieu Dang, Ojaswi Dulal +4
As large language models (LLMs) become widely deployed, their outputs can be copied, transformed, and redistributed at scale without reliable evidence of origin, creating risks for…
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
Truc Nguyen, Phung Lai, Khang Tran +2
Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, w…
XRand: Differentially Private Defense against Explanation-Guided Attacks
Truc Nguyen, Phung Lai, NhatHai Phan +1
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 p…
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Khang Tran, Phung Lai, NhatHai Phan +5
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 dat…
User-Entity Differential Privacy in Learning Natural Language Models
Phung Lai, NhatHai Phan, Tong Sun +4
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 dat…
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning
Phung Lai, Han Hu, NhatHai Phan +3
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. B…
Continual Learning with Differential Privacy
Pradnya Desai, Phung Lai, NhatHai Phan +1
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 t…
Ontology-based Interpretable Machine Learning for Textual Data
Phung Lai, NhatHai Phan, Han Hu +3
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. D…