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Privacy and utility for defect prediction: Experiments with morph (2012)
| Venue: | In ICSE’12 |
| Citations: | 9 - 4 self |
BibTeX
@INPROCEEDINGS{Peters12privacyand,
author = {Fayola Peters and Tim Menzies},
title = {Privacy and utility for defect prediction: Experiments with morph},
booktitle = {In ICSE’12},
year = {2012}
}
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Abstract
Abstract—Ideally, we can learn lessons from software projects across multiple organizations. However, a major impediment to such knowledge sharing are the privacy concerns of software development organizations. This paper aims to provide defect data-set owners with an effective means of privatizing their data prior to release. We explore MORPH which understands how to maintain class boundaries in a data-set. MORPH is a data mutator that moves the data a random distance, taking care not to cross class boundaries. The value of training on this MORPHed data is tested via a 10-way within learning study and a cross learning study using Random Forests, Naive Bayes, and Logistic Regression for ten object-oriented defect data-sets from the PROMISE data repository. Measured in terms of exposure of sensitive attributes, the MORPHed data was four times more private than the unMORPHed data. Also, in terms of the f-measures, there was little difference between the MORPHed and unMORPHed data (original data and data privatized by data-swapping) for both the cross and within study. We conclude that at least for the kinds of OO defect data studied in this project, data can be privatized without concerns for inference efficacy. Keywords-privacy; defect prediction; data mining I.
Keyphrases
defect prediction class boundary morphed data unmorphed data data mutator little difference major impediment naive bayes oo defect data ten object-oriented defect data-sets data mining multiple organization random forest abstract ideally defect data-set owner random distance effective mean original data software development organization privacy concern software project sensitive attribute cross learning study inference efficacy logistic regression promise data repository
