Anonymization ToolBox
In an effort to promote our research in the area of privacy preserving data analysis,
at UT Dallas Data Security and Privacy Lab, we complied our implementation of various
anonymization methods into a toolbox for public use by researchers. The algorithms can either be applied
directly to a dataset or can be used as library functions inside other applications.
The toolbox currently contains 6 different anonymization methods over 3 different privacy definitions:
Together with the anonymization toolbox, we also release the source code of our recent study on classifying anonymized data. In this study, we proposed methods for building distance-based classification models over anonymized data. More specifically, investigated methods include instance-based classifiers and support vector machines.