26 citations · 96 across the 33 of their papers we have counts for
38 papers
HyperZip: Efficient Data Compression through Personalized Diffusion LLMs with Hypernetworks
Thai Nguyen, Khang Tran, NhatHai Phan
Large language models (LLMs) have shown strong potential for lossless data compression, but existing approaches are constrained by the high computational cost and low throughput of…
Poison with Style: A Practical Poisoning Attack on Code Large Language Models
Khang Tran, Yazan Boshmaf, Issa Khalil +3
Code Large Language Models (CLLMs) serve as the core of modern code agents, enabling developers to automate complex software development tasks. In this paper, we present Poison-wit…
Gradient Transformer: Learning to Generate Updates for LLMs
Binh-Nguyen Nguyen, Khang Tran, NhatHai Phan +1
Many organizations lack computational resources to fine-tune large language models (LLMs) on private (unshareable) data for better utility, while fine-tuning tiny language models (…
Robust LLM Watermarking with Minimal Semantic Distortion for IP Protection
Kieu Dang, Phung Lai, NhatHai Phan +2
Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collecting input-output pairs to train a surroga…
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
Khang Tran, Khoa Nguyen, Cristian Borcea +1
Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations. However, they still lack principled mechanisms for…
NOIR: Privacy-Preserving Generation of Code with Open-Source LLMs
Khoa Nguyen, Khiem Ton, NhatHai Phan +6
Although boosting software development performance, large language model (LLM)-powered code generation introduces intellectual property and data security risks rooted in the fact t…
SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning
Khoa Nguyen, Khang Tran, NhatHai Phan +3
This paper proposes Stochastic Geographic Gradient Fusion (SGFusion), a novel training algorithm to leverage the geographic information of mobile users in Federated Learning (FL).…
-STEAL: LLM Stealing Attack with Local Differential Privacy
Kieu Dang, Phung Lai, NhatHai Phan +3
Large language models (LLMs) demonstrate remarkable capabilities across various tasks. However, their deployment introduces significant risks related to intellectual property. In t…
PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
Tuan Nguyen, Naseem Khan, Khang Tran +2
The rapid rise of synthetic media has made deepfake detection a critical challenge for online safety and trust. Progress remains constrained by the scarcity of large, high-quality…
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design
Mahmoud Nazzal, Khoa Nguyen, Deepak Vungarala +4
AI hardware design is advancing rapidly, driven by the promise of design automation to make chip development faster, more efficient, and more accessible to a wide range of users. A…
ViGText: Deepfake Image Detection with Vision-Language Model Explanations and Graph Neural Networks
Ahmad ALBarqawi, Mahmoud Nazzal, Issa Khalil +2
The rapid rise of deepfake technology, which produces realistic but fraudulent digital content, threatens the authenticity of media. Traditional deepfake detection approaches often…
SoK: Are Watermarks in LLMs Ready for Deployment?
Kieu Dang, Phung Lai, NhatHai Phan +4
Large Language Models (LLMs) have transformed natural language processing, demonstrating impressive capabilities across diverse tasks. However, deploying these models introduces cr…
Watermarking Degrades Alignment in Language Models: Analysis and Mitigation
Apurv Verma, NhatHai Phan, Shubhendu Trivedi
Watermarking has become a practical tool for tracing language model outputs, but it modifies token probabilities at inference time, which were carefully tuned by alignment training…
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning
Phung Lai, Guanxiong Liu, NhatHai Phan +3
Federated learning (FL) enables collaborative model training using decentralized private data from multiple clients. While FL has shown robustness against poisoning attacks with ba…
Demo: SGCode: A Flexible Prompt-Optimizing System for Secure Generation of Code
Khiem Ton, Nhi Nguyen, Mahmoud Nazzal +6
This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs). SGCode integrates recent prompt-optimization approaches…
PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)
Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah +1
The capability of generating high-quality source code using large language models (LLMs) reduces software development time and costs. However, they often introduce security vulnera…
Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)
Apurv Verma, Satyapriya Krishna, Sebastian Gehrmann +7
Creating secure and resilient applications with large language models (LLM) requires anticipating, adjusting to, and countering unforeseen threats. Red-teaming has emerged as a cri…
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah +2
Malicious domain detection (MDD) is an open security challenge that aims to detect if an Internet domain is associated with cyber-attacks. Among many approaches to this problem, gr…
FairDP: Certified Fairness with Differential Privacy
Khang Tran, Ferdinando Fioretto, Issa Khalil +2
This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP)…
Zone-based Federated Learning for Mobile Sensing Data
Xiaopeng Jiang, Thinh On, NhatHai Phan +5
Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. H…
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…