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UID:245@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20241202T140000
DTEND;TZID=Europe/Paris:20241202T140000
DTSTAMP:20260825T134515Z
URL:https://isdm.umontpellier.fr/events/privacy-auditing-of-dp-sgd-in-the-
 hidden-state-threat-model/
SUMMARY:Privacy Auditing of DP-SGD in the Hidden State Threat Model
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice\n\nIn this talk\, we introduce pr
 ivacy auditing and explore the privacy guarantees of machine learning mode
 ls trained with Differentially Private Stochastic Gradient Descent (DP-SGD
 )\, focusing on the hidden state threat model\, in which an adversary only
  has access to the final training model. We employ simple adversaries for 
 privacy auditing models in the hidden state\, outperforming previous metho
 ds. We show that if a user contributes to each DP-SGD step\, releasing onl
 y the final model does not amplify privacy\, compared to the standard case
  where all intermediary models are released. The problem is more complex w
 hen the crafted gradient is not inserted at every step: our auditing lower
  bound matches the privacy upper bound only for an adversarially-chosen lo
 ss landscape and a sufficiently large batch size. This suggests that exist
 ing privacy upper bounds can be improved in certain regimes\n\nMachine Lea
 rning in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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 ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.124 Building 5:geo
 :0,0
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DTSTART:20241027T020000
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