9 citations · 10 across the 5 of their papers we have counts for
5 papers
Instance-Level Data-Use Auditing of Visual ML Models
Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter
The growing trend of legal disputes over the unauthorized use of data in machine learning (ML) systems highlights the urgent need for reliable data-use auditing mechanisms to ensur…
A General Framework for Data-Use Auditing of ML Models
Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter
Auditing the use of data in training machine-learning (ML) models is an increasingly pressing challenge, as myriad ML practitioners routinely leverage the effort of content creator…
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
Yujie Zhang, Neil Gong, Michael K. Reiter
Federated Learning (FL) is a decentralized machine learning method that enables participants to collaboratively train a model without sharing their private data. Despite its privac…
Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Hongbin Liu, Michael K. Reiter, Neil Zhenqiang Gong
Foundation model has become the backbone of the AI ecosystem. In particular, a foundation model can be used as a general-purpose feature extractor to build various downstream class…
Mendata: A Framework to Purify Manipulated Training Data
Zonghao Huang, Neil Gong, Michael K. Reiter
Untrusted data used to train a model might have been manipulated to endow the learned model with hidden properties that the data contributor might later exploit. Data purification…