Secure Deep Graph Generation with Link Differential Privacy
Authors: Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, Lichao Sun
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy. |
| Researcher Affiliation | Academia | Carl Yang1 , Haonan Wang2 , Ke Zhang3 , Liang Chen4 , Lichao Sun5 1Emory University 2University of Illinois at Urbana Champaign 3University of Hong Kong 4Sun Yat-sen University 5Lehigh University |
| Pseudocode | Yes | Algorithm 1 DPGGAN |
| Open Source Code | Yes | All code and data are in the Supplementary Materials accompanying the submission. |
| Open Datasets | Yes | To provide a side-to-side comparison between the original networks and generated networks, we use two standard datasets of real-world networks, i.e., DBLP, and IMDB. |
| Dataset Splits | No | The paper mentions training models but does not provide specific training/validation/test dataset splits (e.g., percentages or sample counts) for its main experiments. It describes a sampling strategy for batch size and a specific setup for link prediction evaluation but not overall dataset splits for reproduction. |
| Hardware Specification | Yes | All experiments are done with four Ge Force GTX 1080 GPUs and a 12-core 2.2GHz CPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies (e.g., Python, PyTorch, TensorFlow versions or specific libraries with their versions). |
| Experiment Setup | Yes | For GVAE and our models, we use two-layer GCNs with sizes 32 16 for both gµ and gσ of the encoder network, where the first layer is shared. We use two-layer FNNs with sizes 16 32 for f of the decoder (generator) network. For DPGGAN, we use another two-layer GCN with the same sizes for g and a three-layer FNN with sizes 16 32 1 for f . For DP-related hyper-parameters, we follow existing works [Abadi et al., 2016; Shokri and Shmatikov, 2015] to fix δ to 10-5, σ to 5, and q to 0.01 (which determines the batch size B as B = q N with N as the graph size). ... we empirically set the clipping hyper-parameter C to 5, decay ratio γ to 0.99, learning rate η to 10-3, and the loss weighing hyper-parameters λ1 and λ2 both to 0.1. |