NhatHai Phan | DeepAI

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∙ 03/10/2023

Zone-based Federated Learning for Mobile Sensing Data

Mobile apps, such as mHealth and wellness applications, can benefit from...

0 Xiaopeng Jiang, et al. ∙

∙ 02/24/2023

Active Membership Inference Attack under Local Differential Privacy in Federated Learning

Federated learning (FL) was originally regarded as a framework for colla...

0 Truc Nguyen, et al. ∙

∙ 12/08/2022

XRand: Differentially Private Defense against Explanation-Guided Attacks

Recent development in the field of explainable artificial intelligence (...

0 Truc Nguyen, et al. ∙

∙ 11/10/2022

Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks

Graph neural networks (GNNs) are susceptible to privacy inference attack...

0 Khang Tran, et al. ∙

∙ 11/01/2022

User-Entity Differential Privacy in Learning Natural Language Models

In this paper, we introduce a novel concept of user-entity differential ...

0 Phung Lai, et al. ∙

∙ 07/26/2022

Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning

In this paper, we show that the process of continually learning new task...

1 Phung Lai, et al. ∙

∙ 01/18/2022

Model Transferring Attacks to Backdoor HyperNetwork in Personalized Federated Learning

This paper explores previously unknown backdoor risks in HyperNet-based ...

0 Phung Lai, et al. ∙

∙ 11/17/2021

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

This paper presents the design, implementation, and evaluation of FLSys,...

0 Han Hu, et al. ∙

∙ 10/11/2021

Continual Learning with Differential Privacy

In this paper, we focus on preserving differential privacy (DP) in conti...

0 Pradnya Desai, et al. ∙

∙ 09/03/2021

A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples

In this work, we show how to jointly exploit adversarial perturbation an...

0 Guanxiong Liu, et al. ∙

∙ 04/01/2020

Ontology-based Interpretable Machine Learning for Textual Data

In this paper, we introduce a novel interpreting framework that learns a...

0 Phung Lai, et al. ∙

∙ 06/05/2019

Evaluating Explainers via Perturbation

Due to high complexity of many modern machine learning models such as de...

0 Minh N. Vu, et al. ∙

∙ 06/02/2019

Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness

In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM)...

0 NhatHai Phan, et al. ∙

∙ 03/23/2019

Preserving Differential Privacy in Adversarial Learning with Provable Robustness

In this paper, we aim to develop a novel mechanism to preserve different...

0 NhatHai Phan, et al. ∙

∙ 09/18/2017

Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

In this paper, we focus on developing a novel mechanism to preserve diff...

0 NhatHai Phan, et al. ∙

∙ 06/25/2017

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare d...

0 NhatHai Phan, et al. ∙