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  • title: DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-preserving Data Generation
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            DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-preserving Data Generation
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            DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-preserving Data Generation

            Apr 14, 2021

            Speakers

            FH

            Frederik Harder

            Speaker · 1 follower

            KA

            Kamil Adamczewski

            Speaker · 1 follower

            MP

            Mijung Park

            Speaker · 0 followers

            About

            We propose a differentially private data generation paradigm using random feature representations of kernel mean embeddings when comparing the distribution of true data with that of synthetic data. We exploit the random feature representations for two important benefits. First, we require a minimal privacy cost for training deep generative models. This is because unlike kernel-based distance metrics that require computing the kernel matrix on all pairs of true and synthetic data points, we can d…

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