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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Canada/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
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TZOFFSETTO:-0400
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BEGIN:STANDARD
DTSTART:20211107T010000
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BEGIN:VEVENT
DTSTAMP:20211130T042750Z
UID:F35653A5-5F81-40C8-8512-E397ABE65BA2
DTSTART;TZID=Canada/Eastern:20211105T150000
DTEND;TZID=Canada/Eastern:20211105T160000
DESCRIPTION:When sharing personal data publicly\, it is promised that ident
 ity of the people whose data is published will remain hidden. Anonymizatio
 n is necessary but not enough\, such that there are many examples of re-id
 entification on anonymized databases. The issue is not limited to the data
 \, but also to models as well when shared publicly and trained on personal
  data with potentially sensitive contents such as credit card numbers or i
 ndustrial strategies. Such discussions are the main topics in the context 
 of privacy.\n\nSpeaker(s): Mehdi Amian\, \n\nVirtual: https://events.vtool
 s.ieee.org/m/287722
LOCATION:Virtual: https://events.vtools.ieee.org/m/287722
ORGANIZER:moein.noferesti@gmail.com
SEQUENCE:3
SUMMARY:Privacy in data and in machine learning models
URL;VALUE=URI:https://events.vtools.ieee.org/m/287722
X-ALT-DESC:Description: <br /><p><span style="font-family: 'Calibri'\,sans-
 serif\; color: black\;">When sharing personal data publicly\, it is promis
 ed that identity of the people whose data is published will remain hidden.
  Anonymization is necessary but not enough\, such that there are many exam
 ples of re-identification on anonymized databases. The issue is not limite
 d to the data\, but also to models as well when shared publicly and traine
 d on personal data with potentially sensitive contents such as credit card
  numbers or industrial strategies. Such discussions are the main topics in
  the context of privacy.&nbsp\;</span></p>\n<p><span style="font-family: '
 Calibri'\,sans-serif\; color: black\;">&nbsp\;</span></p>
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