Distributed Deep Variational Approach for Privacy-preserving Data Release
Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical sensors, IoT devices, wearables the protection offered by keeping data local is incomplete: gradients, model updates, and the released representations themselves can leak sensitive attributes. We propose the \emph{Gaussian Privacy Protector} (GPP), a data-release framework for continuous, high-dimensional inputs that learns a stochastic encoder mapping raw data to a low-dimensional sanitized representation. The encoder is trained against a variational lower bound on the mutual information between the released representation and a designated se...
| Authors | Zahir Alsulaimawi, Huaping Liu |
|---|---|
| Primary category | cs.CR |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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