Towards practical differentially private causal graph discovery
Authors: Lun Wang, Qi Pang, Dawn Song
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated Priv-PC on 7 public datasets and compared with the state-of-the-art. The results show that Priv-PC achieves 10.61 to 293.87 times speedup and better utility. |
| Researcher Affiliation | Academia | Lun Wang University of California, Berkeley wanglun@berkeley.edu Qi Pang Zhejiang University pangqi@zju.edu.cn Dawn Song University of California, Berkeley dawnsong@gmail.com |
| Pseudocode | Yes | Algorithm 1: One-off sieve-and-examine mechanism. Algorithm 2: Priv-PC Algorithm with Kendall s τ. |
| Open Source Code | Yes | The implementation of Priv-PC, including the code used in our evaluation, is available at https://github.com/sunblaze-ucb/ Priv-PC-Differentially-Private-Causal-Graph-Discovery. |
| Open Datasets | Yes | We evaluated Priv-PC on 7 public datasets... The detailed information about the datasets is shown in Table 1. Dataset Type: Earthquake [15], Cancer [15], Asia [18], Survey [27], Alarm [3], Sachs [26], Child [4]. |
| Dataset Splits | No | The paper does not provide explicit training/test/validation dataset splits, such as percentages or sample counts for each split. |
| Hardware Specification | Yes | All the experiments were run on a Ubuntu18.04 LTS server with 32 AMD Opteron(TM) Processor 6212 with 512GB RAM. |
| Software Dependencies | No | The paper mentions running experiments on "Ubuntu18.04 LTS" but does not specify versions for any other ancillary software, libraries, or programming languages. |
| Experiment Setup | Yes | To directly compare EM-PC and Priv-PC, we ran the two algorithms on the datasets with 21 different privacy parameters and presented the results with accumulated privacy cost between 1 and 100. Furthermore, to demonstrate the utility improvement due to sieve-and-examine, we also directly applied sparse vector technique to PC algorithm (SVT-PC) and evaluated it under the same setting. For each privacy parameter, we ran the three algorithms for 5 times and recorded the mean and standard deviation of the utility of the output graph and the running time. We fix δ = 1e-3 for both EM-PC and Priv-PC across all the experiments. |