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.