2 citations · 5 across the 6 of their papers we have counts for
6 papers
DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders
Syed Irfan Ali Meerza, Oktay Ozturk, Amir Sadovnik +1
AI models are increasingly trained on personal images scraped from social media and public platforms, often without consent, leading to serious privacy violations, such as unauthor…
Auditing Training Data in Generative Music Models via Black-Box Membership Inference
Yi Chen Liu, Jiawei Yu, Kexin Cao +3
Recent advances in text-to-music generation enable high-fidelity synthesis of structured musical audio, raising growing concerns about data provenance, consent, and training transp…
FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs
Syed Irfan Ali Meerza, Feiyi Wang, Jian Liu
Given the growing reliance on private data in training Large Language Models (LLMs), Federated Learning (FL) combined with Parameter-Efficient Fine-Tuning (PEFT) has garnered signi…
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
Syed Irfan Ali Meerza, Jian Liu
Federated Learning (FL) is a technique that allows multiple parties to train a shared model collaboratively without disclosing their private data. It has become increasingly popula…
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang +1
Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. However, the federa…
Optimal Scheduling of Anticipated COVID-19 Vaccination: A Case Study of New York State
Syed Irfan Ali Meerza, Seyed M. Karimi, Bert B. Little +2
This study aims to determine an optimal control strategy for vaccine scheduling in COVID-19 pandemic treatment by converting widely acknowledged infectious disease model named SEIR…