Alleviating Privacy Attacks via Causal Learning
Authors: Shruti Tople, Amit Sharma, Aditya Nori
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on two types of datasets: 1) Four datasets generated from known Bayesian Networks and 2) Colored images of digits from the MNIST dataset. Code is available at https://github.com/microsoft/robustdg. ... 4.1 Results for Bayesian Network Datasets ... 4.2 Results for Colored MNIST Dataset |
| Researcher Affiliation | Industry | 1Microsoft Research. Correspondence to: Shruti Tople <shruti.tople@microsoft.com>, Amit Sharma <amshar@microsoft.com>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/microsoft/robustdg. |
| Open Datasets | Yes | We select 4 Bayesian network datasets Child, Sachs, Alarm and Water that range from 178 10k parameters (Table 1)2. ... 2www.bnlearn.com/bnrepository. ... For this, we consider colored MNIST images used in a recent work by Arjovsky et al. (2019). ... 3http://yann.lecun.com/exdb/mnist/ 4https://github.com/facebookresearch/Invariant Risk Minimization. |
| Dataset Splits | No | The paper states: 'We sample data using the causal structure and probabilities from the Bayesian network, and use a 60 : 40% split for train-test datasets.' However, it does not explicitly provide information about a separate validation split or its size/percentage for either dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | Yes | To train the causal model, we use the bnlearn library in R language... To train the DNN model and the attacker model, we build custom estimators in Python using Tensorflow v1.2. |
| Experiment Setup | Yes | The DNN model is a multilayer perceptron (MLP) with 3 hidden layers of 128, 512 and 128 nodes respectively. The learning rate is set to 0.0001 and the model is trained for 10000 steps. The attacker model has 2 hidden layers with 5 nodes each, a learning rate of 0.001, and is trained for 5000 steps. Both models use Adam optimizer, Re LU for the activation function, and cross entropy as the loss function. |