An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
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Latent Dirichlet Allocation (LDA) is a powerful probabilistic model used to cluster documents based on thematic structure. We provide end-to-end analysis of differentially private LDA learning models, based on a spectral algorithm with established theoretically guaranteed utility. The spectral algorithm involves a complex data flow, with multiple options for noise injection. We analyze the sensitivity and utility of different configurations of noise injection to characterize configurations that achieve least performance degradation under different operating regimes.
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