2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Noise Masking Attacks and Defenses for Pretrained Speech Models
Matthew Jagielski, Om Thakkar, Lun Wang
Speech models are often trained on sensitive data in order to improve model performance, leading to potential privacy leakage. Our work considers noise masking attacks, introduced…
cs.LG2023
Unintended Memorization in Large ASR Models, and How to Mitigate It
Lun Wang, Om Thakkar, Rajiv Mathews
It is well-known that neural networks can unintentionally memorize their training examples, causing privacy concerns. However, auditing memorization in large non-auto-regressive au…
cs.LG2023★ 2 cited
Why Is Public Pretraining Necessary for Private Model Training?
Arun Ganesh, Mahdi Haghifam, Milad Nasr +5
In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks, remarkable improvements have been widely reported with the use of pretraining on publicly…