Fayola Peters, Tim Menzies, Lucas Layman
F. Peters, T. Menzies, and L. Layman, “LACE2: Better Privacy-Preserving Data Sharing for Cross Project Defect Prediction,” in 37th International Conference on Software Engineering (ICSE ’15), (Florence, Italy), pp. 801–811, 2015
Publication year: 2015

Before a community can learn general principles, it must share individual experiences. Data sharing is the fundamental step of cross project defect prediction, i.e. the process of using data from one project to predict for defects in another. Prior work on secure data sharing allowed data owners to share their data on a single-party basis for defect prediction via data minimization and obfuscation. However the studied method did not consider that bigger data required the data owner to share more of their data.

In this paper, we extend previous work with LACE2 which reduces the amount of data shared by using multi-party data sharing. Here data owners incrementally add data to a cache passed among them and contribute “interesting” data that are not similar to the current content of the cache. Also, before data owner i passes the cache to data owner j, privacy is preserved by applying obfuscation algorithms to hide project details. The experiments of this paper show that (a) LACE2 is comparatively less expensive than the single-party approach and (b) the multi-party approach of LACE2 yields higher privacy than the prior approach without damaging predictive efficacy (indeed, in some cases, LACE2 leads to better defect predictors).