3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CR2024
Towards a Re-evaluation of Data Forging Attacks in Practice
Mohamed Suliman, Anisa Halimi, Swanand Kadhe +2
Data forging attacks provide counterfactual proof that a model was trained on a given dataset, when in fact, it was trained on another. These attacks work by forging (replacing) mi…
cs.LG2022★ 3 cited
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction
Mohamed Suliman, Douglas Leith
In this paper we present new attacks against federated learning when used to train natural language text models. We illustrate the effectiveness of the attacks against the next wor…