Arx-ASyH-Comparison

Code accompanying the publication "Anonymize or Synthesize? - Privacy-Preserving Methods for Heart Failure Score Analytics", used for comparing current anonymization techniques with recent AI-driven data synthetisation algorithms, and combining the two methods to produce double-processed privacy preserving datasets.

Requirements

• python 3.10
• R, Version >= 4.3.0
• JDK 17
• ASyH 1.0.0

Installation

1. Create a virtual environment and install the necessary python packages according to the requirement documents
2. Under Windows: Remove the python-magic package and make sure python-magic-bin is installed
3. Activate the virtual environment and install the required R packages by using Rscript Install_R_packages.R. Make sure R_HOME environment variable is set to the R root folder.

Usage

Go to the top directory of the cloned AnonymizeAndSynthesize copy, create a subdirectory data and add the original Dataset to anonymize/synthesize as data/UCC_heart_data.csv.

Data Generation and Utility Analysis

With the basis dataset in place and being in the top directory of the cloned copy, issue the following command:

python3 ./script_utility_analysis.py --input_original data/UCC_heart_data.csv --output <output_directory>

replace <output_directory> with the path to which you want to have the output files written. This will produce an anonymized, a synthetic, and a synthesized anonymized dataset for MAGGIC and BioHF separately, and will create fidelity and utility analysis data, comparing ecdf plots and violin plots of the data distributions of all datasets.

File System Structure of Output

<output_directory>/BioHF/<date>_comparison_statistics_BIOHF_cat.csv
<output_directory>/BioHF/<date>_comparison_statistics_BIOHF_cont.csv
<output_directory>/BioHF/<date>_ecdf_orig_anon_synth-biohf_v1_1.eps
<output_directory>/BioHF/<date>_violin_anon_orig_synth-biohf_v1_1.eps
<output_directory>/MAGGIC/<date>_comparison_statistics_MAGGIC_cat.csv
<output_directory>/MAGGIC/<date>_ecdf_orig_anon_synth-maggic_score_1.eps
<output_directory>/MAGGIC/<date>_comparison_statistics_MAGGIC_cont.csv
<output_directory>/MAGGIC/<date>_violin_anon_orig_synth-maggic_score_1.eps

Data Generation and Re-Identification Risk Analysis

For a risk analysis, analogue to the Utility Analysis script, when in the cloned AnonymizeAndSynthesize copy's top directory, you can run

python3 ./script_risk_analysis.py --input_original data/UCC_heart_data.csv --output <output_directory>

(again, adjust the <output_directory> argument).

The risk analysis data can be found under

<output_directory>/BioHF/Risk Assessment/<date>_UCC_heart_data_control_combined.csv
<output_directory>/BioHF/Risk Assessment/<date>_UCC_heart_data_training_combined.csv
<output_directory>/BioHF/Risk Assessment/<date>_UCC_heart_data_control.csv
<output_directory>/BioHF/Risk Assessment/<date>_UCC_heart_data_training.csv
<output_directory>/BioHF/Risk Assessment/<date>_UCC_heart_data_train_anonymized.csv
<output_directory>/BioHF/Risk Assessment/<date>_UCC_heart_data_train_synthetic.csv
<output_directory>/MAGGIC/risk/<date>_UCC_heart_data_control_combined.csv
<output_directory>/MAGGIC/risk/<date>_UCC_heart_data_training_combined.csv
<output_directory>/MAGGIC/risk/<date>_UCC_heart_data_control.csv
<output_directory>/MAGGIC/risk/<date>_UCC_heart_data_training.csv
<output_directory>/MAGGIC/risk/<date>_UCC_heart_data_train_anonymized.csv
<output_directory>/MAGGIC/risk/<date>_UCC_heart_data_train_synthetic.csv

Input Dataset Layout

The following columns are mandatory for the input data csv file to be processed for both MAGGIC and BioHF scores:

variable	type
"age"	numerical
"gender"	[m | f | N/A ]
"bmi"	numerical
"sys_bp_m"	numerical
"nyha"	[ I | II | III | IV | N/A ]
"smoking"	[ 0. | 1. | N/A ]
"diabetes"	[ 0. | 1. | N/A ]
"copd"	[ 0. | 1. | N/A ]
"hf_duration"	numerical
"hf_gt_18_months"	[ 0. | 1. | N/A ]
"mra"	[ 0. | 1. | N/A ]
"beta"	[ 0. | 1. | N/A ]
"furosemide1"	[ 0. | 1. | N/A ]
"statin"	[ 0. | 1. | N/A ]
"arni"	[ 0. | 1. | N/A ]
"acei_arb"	[ 0. | 1. | N/A ]
"lvef_m"	numerical
"creatinine_m"	numerical
"sodium_m"	numerical
"hb_m"	numerical
"egfr_m"	numerical
"ntprobnp_m"	numerical
"hstnt_m"	numerical