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UID:401@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20241119T140000
DTEND;TZID=Europe/Paris:20241119T140000
DTSTAMP:20260828T115135Z
URL:https://isdm.umontpellier.fr/events/introduction-to-differentially-pri
 vate-machine-learning-2/
SUMMARY:Introduction to Differentially Private Machine Learning
DESCRIPTION:Room 02.022\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice\n\nPersonal data is being collect
 ed at an unprecedented scale by businesses and public organizations\, driv
 en by the progress of data science and machine learning. While such data c
 an be turned into useful knowledge about the global population by computin
 g aggregate statistics or training machine learning models\, this can also
  lead to undesirable disclosure of personal information. We must therefore
  deal with two conflicting objectives: maximizing the utility of data whil
 e protecting the privacy of individuals whose data is used in the analysis
 . In this talk\, I will present differential privacy (DP)\, a statistical 
 definition of privacy which comes with rigorous guarantees as well as an a
 lgorithmic framework that allows the design of practical privacy-preservin
 g algorithms. I will then discuss the application of DP to machine learnin
 g\, and some related open questions.\n\nMachine Learning 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 02.022\, Building 5\, St Priest Campus\, Montpellier\, 34000\
 , France
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 ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.022\, Building 5:g
 eo:0,0
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