Anonymization of high-dimensional datasets

24 mins 58 secs, 45.67 MB, MP3 44100 Hz, 249.75 kbits/sec

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Description:	Loukides, G (Cardiff University) Thursday 8th December 2016 - 15:30 to 16:00


Created:	2016-12-19 12:21
Collection:	Data Linkage and Anonymisation
Publisher:	Isaac Newton Institute
Copyright:	Loukides, G
Language:	eng (English)


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.

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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