11 citations · 26 across the 3 of their papers we have counts for
3 papers
Memorization in NLP Fine-tuning Methods
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang +2
Large language models are shown to present privacy risks through memorization of training data, and several recent works have studied such risks for the pre-training phase. Little…
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal +2
The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy v…
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?
Archit Uniyal, Rakshit Naidu, Sasikanth Kotti +4
Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on dif…