Anshuman Suri
Anshuman Suri
Home
Publications
Posts
Contact
News
Talks
Light
Dark
Automatic
3
Do Parameters Reveal More than Loss for Membership Inference?
We show how prior claims about black-box access sufficing for optimal membership inference do not hold for most useful settings such as SGD, and validate our findings with a new white-box inference attack.
Anshuman Suri
,
Xiao Zhang
,
David Evans
PDF
Cite
Code
SoK: Memorization in General-Purpose Large Language Models
We explore the memorization capabilities of Large Language Models (LLMs), categorizing them into six types, and discuss their implications and challenges.
Valentin Hartmann
,
Anshuman Suri
,
Vincent Bindschaedler
,
David Evans
,
Shruti Tople
,
Robert West
PDF
Cite
Subject Membership Inference Attacks in Federated Learning
We propose a notion of neuron sensitivity in terms of adversarial robustness, along with an attack that works as well as PGD. The notion can be extended as a regularization term, providing adversarial robustness without adversarial training.
Anshuman Suri
,
Pallika Kanani
,
Virendra J. Marathe
,
Daniel W. Peterson
PDF
Cite
One Neuron to Fool Them All
We propose a notion of neuron sensitivity in terms of adversarial robustness, along with an attack that works as well as PGD. The notion can be extended as a regularization term, providing adversarial robustness without adversarial training.
Anshuman Suri
,
David Evans
PDF
Cite
Code
Cite
×