C4.5: programs for machine learning
C4.5: programs for machine learning
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
A framework for privacy preserving classification in data mining
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Privacy in Statistical Databases: CASC Project International Workshop, PSD 2004, Barcelona, Spain, June 9-11, 2004, Proceedings (Lecture Notes in Computer Science)
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A reconstruction-based algorithm for classification rules hiding
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Privacy Preserving Data Mining Research: Current Status and Key Issues
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
| Hi-index | 0.01 |
In this paper, we propose a method of hiding sensitive classification rules from data mining algorithms for categorical datasets. Our approach is to reconstruct a dataset according to the classification rules that have been checked and agreed by the data owner for releasing to data sharing. Unlike the other heuristic modification approaches, firstly, our method classifies a given dataset. Subsequently, a set of classification rules is shown to the data owner to identify the sensitive rules that should be hidden. After that we build a new decision tree that is constituted only non-sensitive rules. Finally, a new dataset is reconstructed. Our experiments show that the sensitive rules can be hidden completely on the reconstructed datasets. While non-sensitive rules are still able to discovered without any side effect. Moreover, our method can also preserve high usability of reconstructed datasets.