PrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices

TL;DR

PrivAgE is a toolchain enabling privacy-preserving data aggregation on edge devices, combining secure summation and differential privacy, with demonstrated efficiency and flexibility for various data analysis tasks.

Contribution

We introduce PrivAgE, a novel toolchain that facilitates privacy-preserving distributed aggregation on resource-constrained edge devices, extending secure summation to clustering.

Findings

Efficient power consumption and bandwidth usage demonstrated on real devices.

Secure summation combined with differential privacy ensures data confidentiality.

Flexible extension to distributed clustering shown in experiments.

Abstract

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.