Data anonymization using k-Anonymity

✔️ Experiments

• Provides 5 k-anonymization method:
  • Datafly
  • Incognito
  • Topdown Greedy
  • Classic Mondrian
  • Basic Mondrian
  • OLA (Optimal Lattice Anonymization)
• Implements 3 anonymization metrics:
  • Equivalent Class size metric (CAVG)
  • Discernibility Metric (DM)
  • Normalized Certainty Penalty (NCP)
• Implements 3 classification models:
  • Random Forests
  • Support Vector Machines
  • K-Nearest Neighbors

📖 Reports

• Report edit link: link
• Slide link: link

Folder Structure

• A dataset must comes with a .csv file contains features information and a hierarchy folder which contains predefined generalization hierarchies for its QID attributes.

this repo
│   anonymize.py
|
└───data  
│   │
│   └───adult
│       │   adult.csv
│       └───hierarchies
│       │     adult_hierarchy_workclass.csv
│       │     ....

• Here is an example for a generalization hierarchy of the 'workclass' attribute from ADULT dataset, described in adult_hierarchy_workclass.csv, which is a csv file using ";" as delimiter

Private;Non-Government;*
Self-emp-not-inc;Non-Government;*
Self-emp-inc;Non-Government;*
Federal-gov;Government;*
Local-gov;Government;*
State-gov;Government;*
Without-pay;Unemployed;*
Never-worked;Unemployed;*

which describes this tree:

---

🌟 Executing

To anonymize dataset, run:

python anonymize.py --method=<model_type> --k=<k-anonymity> --dataset=<dataset_name>

• model_type: [mondrian | classic_mondrian | mondrian_ldiv | topdown | cluster | datafly]
• dataset_name: [adult | cahousing | cmc | mgm | informs | italia]

Results will be in results/{dataset}/{method} folder

To run evaluation metrics on every combination of algorithms, datasets and value k, run:

python visualize.py

Results will be in demo/{metrics.png, metrics_ml.png}

K-Anonymity examples

Before anonymization	After anonymization with k = 2

Evaluation Metrics

Evaluate anonymization using information loss metrics

Evaluate anonymization using classification models

References:

• Basic Mondrian, Top-Down Greedy, Cluster-based (https://github.com/fhstp/k-AnonML)
• L-Diversity (https://github.com/Nuclearstar/K-Anonymity, https://github.com/qiyuangong/Mondrian_L_Diversity)
• Classic Mondrian (https://github.com/qiyuangong/Mondrian)
• Datafly Algorithm (https://github.com/nazilkbahar/python-datafly)
• Normalized Certainty Penalty from Utility-Based Anonymization for Privacy Preservation with Less Information Loss
• Discernibility, Average Equivalent Class Size from A Systematic Comparison and Evaluation of k-Anonymization Algorithms for Practitioners
• Privacy in a Mobile-Social World
• Code and idea based on k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers