  With mobile phone penetration rates reaching 90%, Consumer Proprietary
Network Information (CPNI) can offer extremely valuable information to
different sectors, including policymakers. Indeed, as part of CPNI, Call Detail
Records have been successfully used to provide real-time traffic information,
to improve our understanding of the dynamics of people's mobility and so to
allow prevention and measures in fighting infectious diseases, and to offer
population statistics. While there is no doubt of the usefulness of CPNI data,
privacy concerns regarding sharing individuals' data have prevented it from
being used to its full potential. Traditional de-anonymization measures, such
as pseudonymization and standard de-identification, have been shown to be
insufficient to protect privacy. This has been specifically shown on mobile
phone datasets. As an example, researchers have shown that with only four data
points of approximate place and time information of a user, 95% of users could
be re-identified in a dataset of 1.5 million mobile phone users. In this
landscape paper, we will discuss the state-of-the-art anonymization techniques
and their shortcomings.
