Privacy in data and in machine learning models

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The event is Hybrid!


When sharing personal data publicly, it is promised that identity of the people whose data is published will remain hidden. Anonymization is necessary but not enough, such that there are many examples of re-identification 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 industrial strategies. Such discussions are the main topics in the context of privacy. 

 



  Date and Time

  Location

  Hosts

  Registration



  • Date: 05 Nov 2021
  • Time: 03:00 PM to 04:00 PM
  • All times are (GMT-05:00) Canada/Eastern
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Zoom link will be sent by email before the event.

  • Starts 29 October 2021 09:30 AM
  • Ends 05 November 2021 02:00 PM
  • All times are (GMT-05:00) Canada/Eastern
  • No Admission Charge


  Speakers

Mehdi Amian Mehdi Amian

Biography:

Mehdi is a graduate student in Telecommunications at INRS-EMT, and currently working on the subject of privacy in deep learning models. His research is mostly on developing privacy-protecting components in deep neural networks.





Vaccination passport is required for in-person attendees who are not enrolled in INRS-EMT. 

Coffee and refreshment will be served.