Presented by:
Grigorios Loukides
Date:
Thursday 8th December 2016 - 15:30 to 16:00
Venue:
INI Seminar Room 1
Abstract:
Organizations collect increasing amounts of high-dimensional data about
individuals. Examples are health record datasets containing diagnosis
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 and from an efficiency point of view. In
this talk, I will illustrate the problem and briefly review the main techniques used in the anonymization of 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.
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