Consumer Data and Privacy in Ubiquitous Computing

Consumer Data and Privacy in Ubiquitous Computing is a Tesis by Teemu Mutanen published by VTT in 2007.

This is the Thesis Abstract

The emergence of ubiquitous computing means new devices, sensors, and protocols throughout society, and thus new sources of consumer data. The new data sources, along with new means of individual identification, constitute a personal privacy concern: what should and should not be done with personal data. The personal-privacy issue is accompanied by corporate privacy when data mining tasks are applied to consumer databases. The ubiquitous-computing environment will provide various data sources, and these databases will be distributed among various agents.


The privacy-preserving perspective on data mining is a relatively young area. The research in this area is mainly theoretical; to the best of our understanding, no real-world applications exist. In this work, we have tried to fill this gap. The current trend in the growing amount of personalization in online services has also created applications for personalized marketing. Personalized marketing services use detailed information about the context and personal history of a customer. This needs sophisticated individual identification methods, which themselves raise privacy concerns. The novelty in privacy-preserving methods is that sensitive and distributed data could be used for data mining tasks while the privacy of individuals is still preserved.


This thesis has two objectives: the first is to use consumer data from distributed sources and study how customer segmentation is possible while preserving privacy. The idea is to conduct the customer segmentation in a way that the data need not leave the agent holding the data. The other objective is the value of the knowledge acquired from collectively conducted segmentation. We believe that collectively conducted segmentation produces knowledge that cannot be acquired otherwise. The results of this work show that privacy-preserving customer segmentation is possible and that collectively conducted segmentation produces new knowledge.