Differentially Private Heavy Hitter Detection using Federated Analytics. (arXiv:2307.11749v1 [cs.LG])

In this work, we study practical heuristics to improve the performance of
prefix-tree based algorithms for differentially private heavy hitter detection.
Our model assumes each user has multiple data points and the goal is to learn
as many of the most frequent data points as possible across all users' data
with aggregate and local differential privacy. We propose an adaptive
hyperparameter tuning algorithm that improves the performance of the algorithm
while satisfying computational, communication and privacy constraints. We
explore the …

algorithms analytics data data points detection federated analytics learn performance private study work