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.

翻译：有价值的见解，例如新冠疫情后频繁访问的场所，通常只能通过分析分布在智能手机等边缘设备上的敏感数据来获取。为促进此类分析，我们提出了一种分布式、隐私保护的本地数据聚合工具链，该工具链充分考虑了边缘设备的有限资源。分布式聚合基于安全求和，同时满足差分隐私的概念。通过这种方式，其他方既无法获知单个客户端的敏感数据，也无法得知单个客户端对最终结果的影响。我们在真实设备和模拟设备上对功耗、运行时间和带宽开销进行了评估，并通过将直方图求和扩展到分布式聚类，展示了工具链的灵活性。