amartyasanyal.bsky.social
Assistant Professor @Dept. Of Computer Science, University of Copenhagen, Ex Postdoc @MPI-IS, ETHZ, PhD @University of Oxford, B.Tech @CSE,IITK.
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#ICLR
»Differentially Private Steering for Large Language Model Alignment« by @anmolgoel.bsky.social, Yaxi Hu, Iryna Gurevych (@igurevych.bsky.social) & Amartya Sanyal (@amartyasanyal.bsky.social)
(2/🧵)
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Thank you!
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Thanks Christoph!
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Thank you!!
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@ccanonne.bsky.social : Steak holders
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And I think a similar argument holds for synthetic data.
Synthetic data algorithms that don't provably account for privacy probably doesn't provide privacy.
But there are private synthetic data generation algorithms that do like @gautamkamath.com linked above.
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Transformations like JL can indeed preserve privacy (arxiv.org/abs/1204.2136), while others may lead to (quantifiable) privacy degradation (arxiv.org/abs/2403.13041).
The point is perhaps that augmentations, by themselves, don’t inherently guarantee an increase or decrease in privacy.
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Graduating with a PhD related to privacy and robustness in machine learning? Apply to this post-doc opening by @amartyasanyal.bsky.social: employment.ku.dk/faculty/?sho...
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Done
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Would love to be added as well if possible