  Valuable insights, such as frequently visited environments in the wake of the
COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data
spread across edge-devices like smartphones. To facilitate such an analysis, we
present a toolchain called PrivAgE for a distributed, privacy-preserving
aggregation of local data by taking the limited resources of edge-devices into
account. The distributed aggregation is based on secure summation and
simultaneously satisfies the notion of differential privacy. In this way, other
parties can neither learn the sensitive data of single clients nor a single
client's influence on the final result. We perform an evaluation of the power
consumption, the running time and the bandwidth overhead on real as well as
simulated devices and demonstrate the flexibility of our toolchain by
presenting an extension of the summation of histograms to distributed
clustering.
