  Today's age of data holds high potential to enhance the way we pursue and
monitor progress in the fields of development and humanitarian action. We study
the relation between data utility and privacy risk in large-scale behavioral
data, focusing on mobile phone metadata as paradigmatic domain. To measure
utility, we survey experts about the value of mobile phone metadata at various
spatial and temporal granularity levels. To measure privacy, we propose a
formal and intuitive measure of reidentification risk$\unicode{x2014}$the
information ratio$\unicode{x2014}$and compute it at each granularity level. Our
results confirm the existence of a stark tradeoff between data utility and
reidentifiability, where the most valuable datasets are also most prone to
reidentification. When data is specified at ZIP-code and hourly levels, outside
knowledge of only 7% of a person's data suffices for reidentification and
retrieval of the remaining 93%. In contrast, in the least valuable dataset,
specified at municipality and daily levels, reidentification requires on
average outside knowledge of 51%, or 31 data points, of a person's data to
retrieve the remaining 49%. Overall, our findings show that coarsening data
directly erodes its value, and highlight the need for using data-coarsening,
not as stand-alone mechanism, but in combination with data-sharing models that
provide adjustable degrees of accountability and security.
