Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection

• URL: http://arxiv.org/abs/2107.13640v1
• Date: Wed, 28 Jul 2021 20:52:28 GMT
• Title: Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection
• Authors: Amit Chaulwar and Michael Huth
• Abstract summary: Federated analytics 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. We propose a protocol, named SAFE, for Bayesian federated analytics that offers sufficient privacy for production grade use cases.
• Score: 3.04585143845864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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