Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection. (arXiv:2107.13640v1 [cs.CR])

Federated analytics has many applications in edge computing, its use can lead
to better decision making for service provision, product development, and user
experience. We propose a Bayesian approach to trend detection in which the
probability of a keyword being trendy, given a dataset, is computed via Bayes’
Theorem; the probability of a dataset, given that a keyword is trendy, is
computed through secure aggregation of such conditional probabilities over
local datasets of users. We propose a protocol, named SAFE, for Bayesian
federated analytics that offers sufficient privacy for production grade use
cases and reduces the computational burden of users and an aggregator. We
illustrate this approach with a trend detection experiment and discuss how this
approach could be extended further to make it production-ready.