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Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection

NeurIPS 2023 · Federated learning and privacy › Differential privacy · approach: Private training with public data · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use decoupled graph convolution instead of standard graph convolutionDP graph learning under edge GDP · Squirrel and Chameleontest accuracy (%): 35.15 $\pm$ 0.47 -> 38.18 $\pm$ 1.48; 49.47 $\pm$ 0.88 -> 53.83 $\pm$ 1.11Helped
Use decoupled graph convolution instead of standard graph convolutionDP graph learning under N1 GDP · Facebook, Computers, Phototest accuracy (%): 51.57 $\pm$ 0.28 -> 80.94 $\pm$ 0.27; 77.50 $\pm$ 0.20 -> 83.49 $\pm$ 0.29; 81.27 $\pm$ 0.31 -> 88.38 $\pm$ 0.44Helped
Use decoupled graph convolution instead of standard graph convolutionDP graph learning under N1 GDP · Pubmed and Coratest accuracy (%): 85.72 $\pm$ 0.11 -> 84.33 $\pm$ 0.40; 64.29 $\pm$ 0.80 -> 59.98 $\pm$ 0.81Hurt
Increase the protected neighborhood granularity kDPDGC · k-neighbor GDPtest accuracy (%): N1 to N5 to N25: Facebook 80.94 $\pm$ 0.27, 76.84 $\pm$ 0.36, 68.66 $\pm$ 0.32Hurt
Use a graph model rather than DP-MLPDPDGC · cSBM with strong graph-topology informationtest accuracyHelped
Use a graph model rather than DP-MLPDPDGC · cSBM with weak graph-topology informationtest accuracyHurt
Use decoupled graph convolution rather than standard graph convolutionDPDGC vs GAP · edge GDP with small privacy budget εutilityHurt
Use decoupled graph convolution rather than standard graph convolutionDPDGC vs GAP · edge GDP with ε≥4utility: ε≥4Helped
Replace standard graph convolution A H with decoupled A W^(A)DPDGC vs GAP · k-neighbor GDPrequired DP noise dependence on k: GAP: 4DLα/(2s^2); DPDGC: kα/(2s^2)Helped
Replace standard graph convolution A H with decoupled A W^(A)DPDGC vs GAP · node GDPrequired DP noise dependence on maximum degree: GAP: 4DLα/(2s^2); DPDGC: 2Dα/(2s^2)Helped

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