Mobile phones and other ubiquitous technologies are
generating vast amounts of high-resolution location data. This
data has been shown to have a great potential for the public good,
e.g. to monitor human migration during crises or to predict the
spread of epidemic diseases. Location data is, however, considered
one of the most sensitive types of data, and a large body of
research has shown the limits of traditional data anonymization
methods for big data. Privacy concerns have so far strongly
limited the use of location data collected by telcos, especially
in developing countries.
In this paper, we introduce OPAL (for OPen ALgorithms),
an open-source, scalable, and privacy-preserving platform for
location data. At its core, OPAL relies on an open algorithm
to extract key aggregated statistics from location data for a
wide range of potential use cases. We first discuss how we
designed the OPAL platform, building a modular and resilient
framework for efficient location analytics. We then describe the
layered mechanisms we have put in place to protect privacy
and discuss the example of a population density algorithm. We
finally evaluate the scalability and extensibility of the platform
and discuss related work.