This repository is an open source python implementation for Clustering based k-Anonymization. I implement this algorithm in python for further study.

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 [3-4], 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).

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[5]). 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).

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:

#Usage: python anonymizer [a | i] [knn | kmember | oka] [k | qi | data]
#a: adult dataset, i: INFORMS ataset
#knn:k-nearest neighbor, kmember: k-member, oka: one time pass k-means algorithm
#k: varying k, qi: varying qi numbers, data: varying size of dataset
# run Mondrian with adult data and oka with K(K=10)
python anonymizer.py a oka 10

# evalution knn by varying k
python anonymized.py a knn k

[1] K. LeFevre, D. J. DeWitt, R. Ramakrishnan. Mondrian Multidimensional K-Anonymity ICDE '06: Proceedings of the 22nd International Conference on Data Engineering, IEEE Computer Society, 2006, 25

[2] K. LeFevre, D. J. DeWitt, R. Ramakrishnan. Workload-aware Anonymization. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2006, 277-286

[3] UTD Anonymization Toolbox

[4] ARX- Powerful Data Anonymization

[5] 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

========================== by Qiyuan Gong [email protected]

2016-1-27