Hilb and iDist

Hilb and iDist are two data anonymization algorithm for relational dataset, proposed by Gabriel Ghinita in his papers[1]. To our knowledge, HILB and IDIST are the best anonymization algorithm for relational dataset, which run very fast at the same time. I got the C++ implementation from Gabriel Ghinita, and covert it to python.(Thanks to Gabriel Ghinita!).

This repository is an open source python implementation for HILB and iDIST. I implement this algorithm in python for further study.

Motivation

Researches on data privacy have lasted for more than ten years, lots of great papers have been published. However, only a few open source projects are available on Internet [2-3], most open source projects are using algorithms proposed before 2004! Fewer projects have been used in real life. Worse more, most people even don't hear about it. Such a tragedy!

I decided to make some effort. Hoping these open source repositories can help researchers and developers on data privacy (privacy preserving data publishing).

Attention

I used both adult and INFORMS dataset in this implementation. For clarification, we transform NCP to percentage. This NCP percentage is computed by dividing NCP value with the number of values in dataset (also called GCP[1]). The range of NCP percentage is from 0 to 1, where 0 means no information loss, 1 means loses all information (more meaningful than raw NCP, which is sensitive to size of dataset).

Usage and Parameters:

My Implementation is based on Python 2.7 (not Python 3.0). Please make sure your Python environment is collectly installed. You can run Mondrian in following steps:

1. Download (or clone) the whole project.

2. Run "anonymized.py" in root dir with CLI.

Parameters:

# run Mondrian with adult data and default K(K=10)

python anonymizer.py 



# run Mondrian with adult data K=20

python anonymized.py a 20

a: adult dataset, i: INFORMS ataset

k: varying k, qi: varying qi numbers, data: varying size of dataset, one: run only once

For more information:

[1] G. Ghinita, P. Karras, P. Kalnis, N. Mamoulis. Fast data anonymization with low information loss. Proceedings of the 33rd international conference on Very large data bases, VLDB Endowment, 2007, 758-769

[2]UTD Anonymization Toolbox

[3]ARX- Powerful Data Anonymization

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by Qiyuan Gong

qiyuangong@gmail.com

2015-1-21