Abstract
Advances in information technology, and its use in research, are increasing both
the need for anonymized data and the risks of poor anonymization. In this thesis,
we point out some questions raised by current anonymization techniques such as a)
support for additional adversary models and the difficulty of measuring privacy pro-
vided, b) flexibility of algorithms-generalizations with respect to a utility cost metric,
and c) working with complex data. To address these issues, a) We propose a human
understandable privacy notion, δ-presence ; b) We increase flexibility by introduc-
ing a new family of algorithms, clustering-based anonymity algorithms and two new
types of generalizations, natural domain generalizations, generalizations with proba-
bility distributions. We also point out weaknesses such as metric-utility anomalies ;
c) We extend the deï¬nitions of current anonymization techniques for multirelational
and spatio-temporal setting by presenting multirelational k-anonymity, and trajectory
anonymity.