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Are Attribute Inference Attacks Just Imputation?

Please refer to main README file for instructions on setup and installation.

To replicate the results of the paper Are Attribute Inference Attacks Just Imputation?, you would need to execute the run_experiments.sh shell script, which runs the main.py multiple times for different hyper-parameter settings and stores the results in the results/$DATASET folder. This is used for plotting the figures/tables in the paper. Note that the execution may take multiple days to complete on a single machine. For instance, for Census19 data set, run the following command:

$ ./run_experiments.sh -s census

Note that the above script also installs all the required dependencies (in case not already installed), except cuda-toolkit and cudnn. For Texas-100X data set, update the hyper-parameters as commented inside the script, and run:

$ ./run_experiments.sh -s texas_100_v2

Plotting the results from the paper

Run interpret_results.py to obtain the plots and tabular results. For instance, to get the results for Census19 data set, run:

$ python3 interpret_results.py census --skew_attribute=0 --attribute=1 --skew_outcome=3 --sensitive_outcome=3 --adv_knowledge='med' --sample_size=50

Other command-line arguments are as follows:

  • --skew_attribute: Attribute on which to skew the non-iid data sampling 0 (population, default), 1 or 2 -- for Census 1: Income and 2: Race, and for Texas 1: Charges and 2: Ethnicity
  • --attribute: Target sensitive attribute. For Census19: 1 (Race), and for Texas-100X: 2 (Ethnicity)
  • --skew_outcome: In case skew_attribute = 1, which outcome the distribution is skewed upon -- For Census Race: 0 (White, default), 1 (Black) or 3 (Asian), and for Texas Ethnicity: 0 (Hispanic, default) or 1 (Not Hispanic)
  • --sensitive_outcome: For Census Race: 0 (White, default), 1 (Black) or 3 (Asian), and for Texas Ethnicity: 0 (Hispanic, default) or 1 (Not Hispanic)
  • --eps: Specifies the epsilon value to be used for private model (default = None), use eps=1 for private model results
  • --adv_knowledge: Distribution knowledge of adversary: knows skewed distribution (low or low2), knows training distribution (med) or knows training data set (high)
  • --sample_size: How many records the adversary knows: 50, 500, 5000 or 50000
  • --banished_records: Whether to get results from model trained after removing vulnerable records