1 research outputs found
A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices
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 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