10 citations · 18 across the 7 of their papers we have counts for
8 papers
Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting
Chenchen Tan, Youyang Qu, Xinghao Li +4
The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Along with this, the potential reten…
DEFENDCLI: {Command-Line} Driven Attack Provenance Examination
Peilun Wu, Nan Sun, Nour Moustafa +2
Endpoint Detection and Response (EDR) solutions embrace the method of attack provenance graph to discover unknown threats through system event correlation. However, this method sti…
From 5G to 6G: A Survey on Security, Privacy, and Standardization Pathways
Mengmeng Yang, Youyang Qu, Thilina Ranbaduge +12
The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences wi…
Decentralized Privacy Preservation for Critical Connections in Graphs
Conggai Li, Wei Ni, Ming Ding +5
Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable inter…
Privacy at a Price: Exploring its Dual Impact on AI Fairness
Mengmeng Yang, Ming Ding, Youyang Qu +3
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maint…
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Youyang Qu, Ming Ding, Nan Sun +3
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing…
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Yichen Wan, Youyang Qu, Wei Ni +3
Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for applicati…
Learn to Unlearn: A Survey on Machine Unlearning
Youyang Qu, Xin Yuan, Ming Ding +3
Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on re…