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SUMMARY:Shyam Narayanan (MIT EECS)
DTSTART:20221006T213000Z
DTEND:20221006T230000Z
DTSTAMP:20260423T035416Z
UID:SPAMS/19
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/SPAMS/19/">A
 n Introduction to Differentially Private Statistics</a>\nby Shyam Narayana
 n (MIT EECS) as part of MIT Simple Person's Applied Mathematics Seminar\n\
 nLecture held in Room: 2 - 132 in the Simons Building.\n\nAbstract\nIn tod
 ay's era of massive data\, various scientific and technological endeavors 
 have relied on machine learning or statistics models trained on users (e.g
 .\, medical results from patient data\, better advertisement algorithms fr
 om phone data\, etc.). Differential Privacy has recently emerged as one of
  the most popular methods to protect the privacy of users. In this talk\, 
 I will be giving an overview of differential privacy and will focus on how
  we can solve various statistical problems\, such as estimating the mean a
 nd covariance of Multivariate Gaussian distributions\, with differential p
 rivacy using few samples. If time permits\, I will describe some more rece
 nt advanced methods of solving these problems with even fewer samples.\n
LOCATION:https://researchseminars.org/talk/SPAMS/19/
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