2 citations · 5 across the 5 of their papers we have counts for
5 papers
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
Abhinav Kumar, George Torres, Noah Guzinski +6
The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless,…
Persistent Backdoor Attacks in Continual Learning
Zhen Guo, Abhinav Kumar, Reza Tourani
Backdoor attacks pose a significant threat to neural networks, enabling adversaries to manipulate model outputs on specific inputs, often with devastating consequences, especially…
Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning
Nexhi Sula, Abhinav Kumar, Jie Hou +2
With the continued advancement and widespread adoption of machine learning (ML) models across various domains, ensuring user privacy and data security has become a paramount concer…
Unveiling the Unseen: Exploring Whitebox Membership Inference through the Lens of Explainability
Chenxi Li, Abhinav Kumar, Zhen Guo +2
The increasing prominence of deep learning applications and reliance on personalized data underscore the urgent need to address privacy vulnerabilities, particularly Membership Inf…
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
Abhinav Kumar, Miguel A. Guirao Aguilera, Reza Tourani +1
The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privac…