Disparate Privacy Vulnerability: Targeted Attribute Inference Attacks and Defenses

Environment Setup:

To set up the environment, create a virtual environment using Conda or Python’s venv, then activate it. Install dependencies with pip install -r requirements.txt. To run Jupyter notebooks, install notebook using pip install notebook. Once done, the environment is ready for running the provided scripts and notebooks. 

Datasets:

• census19.csv contains the Census-19 dataset.
• Adult_35222.csv and Adult_10000.csv contains the Adult dataset partitions for training and testing respectively.
• Instruction for downloading and preprocessing the Texas-100X dataset:

  1. Download the PUDF_base1q2006_tab.txt, PUDF_base2q2006_tab.txt, PUDF_base3q2006_tab.txt and PUDF_base4q2006_tab.txt files from https://www.dshs.texas.gov/center-health-statistics/texas-health-care-information-collection/health-data-researcher-information/texas-hospital-emergency-department-research-data-file-ed-rdf/hospital-discharge-data-public-use-data-file and place them inside the dataset/texas_100_v2 folder.
  2. Run the following command to preprocess the dataset: 

     python preprocess_dataset.py texas_100_v2 --preprocess 1

  3. After that, use the texas_100_v2.csv file for running the experiments.

Codebase:

Source Files:

• data_utils.py contains code to load dataset and preprocess data. It also contains code to sample data matching target correlation at a subgroup level as described in section 6.1 paragraph 'Sampling Technique'.
• model_utils.py contains code to define the model architecture and training hyperparameters.
• experiment_utils.py contains code to setup experiment for a particular scenario.
• attack_utils.py contains the implementation of existing attacks including CSMIA, LOMIA, imputation attack, and Neuron Importance Attack.
• whitebox_attack.py contains helper functions needed to perform the neuron importance attack.
• disparity_inference_utils.py contains the implementation of Confidence Matrix generation (Algorithm 1), Angular Difference computation (Algorithm 2).
• targeted_inference.py contains the implementation of the targeted attribute inference attacks (section 5.3).
• bcorr_utils.py contains the implementation of the sampling stage of the BCorr defense (section 7.2).

Notebooks:

• correlation_vs_attack_performance.ipynb contains the code to run the experiment described in section 4.1 which shows the strong connection between correlation and attack performance.
• angular_difference_by_sex.ipynb contains the code to run the experiment described in section 4.2 which shows how the angular difference can be used to identify vulnerable groups.
• imputation_vs_ai_aux_size_and_distrib_diff.ipynb contains the code to run the experiment described in section 6.2, which compares the performance between ideal imputation and practical imputation attacks.
• disparity_inference.ipynb contains the code to run the experiment described in section 6.3 which shows how the disparity inference attack can be used to rank groups based on their vulnerability.
• targeted_attribute_inference.ipynb contains the code to run the experiments in section 6.4 which shows how the targeted attribute inference attack outperforms their untargeted counterparts and practical imputation attacks.
• mutual_info_reg.ipynb contains the code to run the experiments in section 7.1 which shows the ineffectiveness of existing defenses in mitigating disparate vulnerability.
• bcorr_defense.ipynb contains the code to run the experiments in section 7.2 which shows how the BCorr defense can be used to mitigate the vulnerability of the model to the targeted attribute inference attack.