Partition_for_Transaction is a top-down anonymization algorithm for set-valued data (or transaction), based on local generalization. It works under k-anonymity constrain, and ensure higher data utility than AA[2]. He et al. proposed this algorithm in 2009[1] on VLDB conference, and gave the pseudocode in his papers, the source code(C++ implement) is not available.
This repository is an open source python implement for Partition_for_Transaction. 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).
Run command in your terminal:
# if K is not privoided, then K is set to 10 as default
python anonymizer.py [K]
by Qiyuan Gong qiyuangong@gmail.com
2014-09-12
[1] He, Y. & Naughton, J. F. Anonymization of set-valued data via top-down, local generalization Proc. VLDB Endow., VLDB Endowment, 2009, 2, 934-945
[2] Terrovitis, M.; Mamoulis, N. & Kalnis, P. Local and global recoding methods for anonymizing set-valued data The VLDB Journal, Springer-Verlag New York, Inc., 2011, 20, 83-106