research
∙
08/22/2023
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
Malicious domain detection (MDD) is an open security challenge that aims...
share
research
∙
03/10/2023
Zone-based Federated Learning for Mobile Sensing Data
Mobile apps, such as mHealth and wellness applications, can benefit from...
research
∙
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...
research
∙
12/08/2022
XRand: Differentially Private Defense against Explanation-Guided Attacks
Recent development in the field of explainable artificial intelligence (...
research
∙
11/10/2022
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attack...
research
∙
11/01/2022
User-Entity Differential Privacy in Learning Natural Language Models
In this paper, we introduce a novel concept of user-entity differential ...
research
∙
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...
research
∙
01/18/2022
Model Transferring Attacks to Backdoor HyperNetwork in Personalized Federated Learning
This paper explores previously unknown backdoor risks in HyperNet-based ...
research
∙
11/17/2021
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
This paper presents the design, implementation, and evaluation of FLSys,...
research
∙
10/11/2021
Continual Learning with Differential Privacy
In this paper, we focus on preserving differential privacy (DP) in conti...
research
∙
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...
research
∙
04/01/2020
Ontology-based Interpretable Machine Learning for Textual Data
In this paper, we introduce a novel interpreting framework that learns a...
research
∙
06/05/2019
Evaluating Explainers via Perturbation
Due to high complexity of many modern machine learning models such as de...
research
∙
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)...
research
∙
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...
research
∙
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...
research
∙
06/25/2017