Top_Down_Greedy_Anonymization is a Top-down greedy data anonymization algorithm for relational dataset, proposed by Jian Xu in his papers[1]. The author showed that Top_Down_Greedy_Anonymization can preserve more data utility than Mondrian[2]. The key idea of Top_Down_Greedy_Anonymization is binary split and balance (merge sub-groups or move some records from larger group to smaller group). So it need more running time than Mondrian.
This repository is an open source python implementation for Top_Down_Greedy_Anonymization. I release 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, making the NCP (information loss) more meaningful (NCP=2000 v.s. NCP=20%, the former one is sensitive to size of dataset, the latter one is suit for any dataset). This NCP percentage (also called GCP[5]) is computed by dividing NCP value with the number of values in dataset (the number of values can be treated as losing all information).
My Implementation is based on Python 2.7 (not Python 3.0). Please make sure your Python environment is correctly installed. You can run Mondrian in following steps:
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Download (or clone) the whole project.
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Run "anonymized.py" in root dir with CLI.
python anonymizer.py
python anonymized.py 20
[1] J. Xu, W. Wang, J. Pei, X. Wang, B. Shi, A. W.-C. Fu. Utility-based anonymization using local recoding. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2006, 785-790
[2] LeFevre, Kristen, David J. DeWitt, and Raghu Ramakrishnan. Mondrian multidimensional k-anonymity. Data Engineering, 2006. ICDE'06. Proceedings of the 22nd International Conference on. IEEE, 2006.
[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