14 citations · 31 across the 10 of their papers we have counts for
10 papers
SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
Yashothara Shanmugarasa, Ming Ding, M. A. P Chamikara +1
Large language models (LLMs) are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer signific…
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana +4
In the era of data-driven machine-learning applications, privacy concerns and the scarcity of labeled data have become paramount challenges. These challenges are particularly prono…
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
Kanishka Ranaweera, David Smith, Pubudu N. Pathirana +3
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine lear…
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana +4
Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data while reducing communication costs compared with…
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…
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…
5G-Enabled Pseudonymity for Cooperative Intelligent Transportation System
Nardine Basta, Ming Ding, Muhammad Ikram +1
Cooperative Intelligent Transportation Systems (C-ITS) enable communications between vehicles, road-side infrastructures, and road-users to improve users' safety and to efficiently…
Realistic Differentially-Private Transmission Power Flow Data Release
David Smith, Frederik Geth, Elliott Vercoe +5
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining…
Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula
Hassan Jameel Asghar, Ming Ding, Thierry Rakotoarivelo +2
We propose a generic mechanism to efficiently release differentially private synthetic versions of high-dimensional datasets with high utility. The core technique in our mechanism…