1.6k citations · 2.6k across the 33 of their papers we have counts for
6 papers · 1 filter
Forget Unlearning: Towards True Data-Deletion in Machine Learning
Rishav Chourasia, Neil Shah
Unlearning algorithms aim to remove deleted data's influence from trained models at a cost lower than full retraining. However, prior guarantees of unlearning in literature are fla…
Data Privacy and Trustworthy Machine Learning
Martin Strobel, Reza Shokri
The privacy risks of machine learning models is a major concern when training them on sensitive and personal data. We discuss the tradeoffs between data privacy and the remaining g…
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin +4
We introduce a new class of attacks on machine learning models. We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak signifi…
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Jiayuan Ye, Reza Shokri
Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of t…
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…
What Does it Mean for a Language Model to Preserve Privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah +2
Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context a…