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SUMMARY:Anonymization of high-dimensional datasets - Grigorios Loukides (C
 ardiff University)
DTSTART:20161208T153000Z
DTEND:20161208T160000Z
UID:TALK69396@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Organizations collect increasing amounts of high-dimensional d
 ata about  individuals. Examples are health record datasets containing dia
 gnosis  information\, marketing datasets containing products purchased by 
 customers\, and  web datasets containing check-ins in social networks. The
  sharing of such data  is increasingly needed to support applications and/
 or satisfy policies and  legislation. However\, the high dimensionality of
  data makes their anonymization  difficult\, both from an effectiveness an
 d from an efficiency point of view. In  this talk\, I will illustrate the 
 problem and briefly review the main techniques used in the anonymization o
 f high-dimensional  data. Subsequently\, I will present a class of methods
  we have been developing  for anonymizing complex\, high-dimensional data 
 and their application to the  healthcare domain.&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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