Master Thesis: utility loss in anonymization of semantic data

Field:

Globalization, increasing competition pressure and shorter product lifecycles evoke more comprehensive but though more precise company information needs. Therefore, more unstructured data is rated as relevant (e.g. user opinions). To treat unstructured data semantic technologies should be used.
On the other hand increasing data growth requires increasing care for individual-related data. The information collected about individuals becomes increasingly sensible and could thus confine the opportunies to develop individually. Anonymization techniques can be used in order to protect microdata provided for analytic purposes.

The aim of this master thesis is to quantify the utility loss caused by anonymization of semantic data.

comparison of existent work in the field of quantifying utility loss in anonymization
research of characteristics in the field of anonymization of semantic data and their impact of quantifying utility loss
implementation of two recommended utility loss metrics in an anonymization toolkit
comparative evaluation of these two utility loss metrics and an argumentative discussion
prospect of further research