Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection

2021-07-28 · Amit Chaulwar, Michael Huth

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.

📄 PDF Abstract BibTeX arXiv:2107.13640

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Decision MakingEdge-computingPrivacy Preserving

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