Agusti Solanas Gómez
In this thesis we adress one of the most important problems that affect the Informations Society- the invasion of personal privacy, The thsis takles the problem by means of statistical disclosoure control techniques such as microaggregation. The thesis is divided in four parts: The first part defines types of privacy depending on the point of viex of the study of the estate of the art of statistical disclousure control, microaggregation and kanonymity. The most common microaggregation methods are presented and their main shortcomings are emphasised.
The second part of the tesis presents the main contributions of tha author to the field of microaggregation. In Chapter 4, a new variable-size method for multivariate microaggregation (i.e V-MDAV) is presented. Chapter 5 explains how to solve the multivariate microaggregation problem by means of evolutionary computation techniques. Chapter 6 presents a new tree-based blocking method that allows the management of very large amounts of data in efficient way. Chapter 6 concludes this part by presenting a new microaggregation heuristic (i.e mu-approximation). This is the first heuristic that has a known and provable upper-bound.
The third part of this thesis considers some applications related to ubiquitous computation. Chapter 7 presents a scalable method that protects the privacy of RFID systems by means of Hash-Locks. Chapter 8 studies the main privacy problems related to location- based services, and it proposes a set of protocols that guarantee the privacy of the users of these services Finally, the fourth part of the thesis contains the conclusions and points out the main future challenges which must be faced in each of the studied areas.