C4.5: programs for machine learning
C4.5: programs for machine learning
A unified framework for enforcing multiple access control policies
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parsimonious downgrading and decision trees applied to the inference problem
Proceedings of the 1998 workshop on New security paradigms
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Data mining, national security, privacy and civil liberties
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A stratification-based approach for handling conflicts in access control
Proceedings of the eighth ACM symposium on Access control models and technologies
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Hiding classification rules for data sharing with privacy preservation
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Pufferfish: A framework for mathematical privacy definitions
ACM Transactions on Database Systems (TODS)
Solving inverse frequent itemset mining with infrequency constraints via large-scale linear programs
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Data sharing between two organizations is common in many application areas e.g. business planing or marketing. Useful global patterns can be discovered from the integrated dataset. However, some sensitive patterns that should have been kept private could also be discovered. In general, disclosure of sensitive patterns could decrease the competitive ability of the data owner. Therefore, sensitive patterns should be hidden before data sharing starts. To address this problem, released datasets must be modified unavoidably. However, if the overall characteristics of the dataset can be maintained, the dataset is still usable perfectly. Therefore, not only the privacy should be concerned, but also the usability. In this paper, we propose a new algorithm to preserve the privacy of the classification rules by using reconstruction technique for categorical datasets. Firstly, all discovered classification rules in the released dataset are presented to the data owner to identify sensitive rules that should be hidden. Subsequently, remained non-sensitive rules along with extracted characteristics information of the dataset are used to build a decision tree. Finally, the new dataset which contains only non-sensitive classification rules is reconstructed from the tree. From empirical studies, our algorithm can preserve the privacy effectively. Additionally, the usability of the datasets can also be preserved.