  Big data applications offer smart solutions to many urgent societal
challenges, such as health care, traffic coordination, energy management, etc.
The basic premise for these applications is "the more data the better". The
focus often lies on sensing infrastructures in the public realm that produce an
ever-increasing amount of data. Yet, any smartphone and smartwatch owner could
be a continuous source of valuable data and contribute to many useful big data
applications. However, such data can reveal a lot of sensitive information,
like the current location or the heart rate of the owner of such devices.
Protection of personal data is important in our society and for example
manifested in the EU General Data Protection Regulation (GDPR). However,
privacy protection and useful big data applications are hard to bring together,
particularly in the human-centered IoT. Implementing proper privacy protection
requires skills that are typically not in the focus of data analysts and big
data developers. Thus, many individuals tend to share none of their data if in
doubt whether it will be properly protected. There exist excellent privacy
solutions between the "all or nothing" approach. For example, instead of
continuously publishing the current location of individuals one might aggregate
this data and only publish information of how many individuals are in a certain
area of the city. Thus, personal data is not revealed, while useful information
for certain applications like traffic coordination is retained. The goal of the
Parrot project is to provide tools for real-time data analysis applications
that leverage this "middle ground". Data analysts should only be required to
specify their data needs, and end-users can select the privacy requirements for
their data as well as the applications and end-users they want to share their
data with.
