Views for Multilevel Database Security
IEEE Transactions on Software Engineering - Special issue on computer security and privacy
A unified framework for enforcing multiple access control policies
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Approximation algorithms
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy in Statistical Databases: CASC Project International Workshop, PSD 2004, Barcelona, Spain, June 9-11, 2004, Proceedings (Lecture Notes in Computer Science)
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
Data Quality in Privacy Preservation for Associative Classification
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
A Novel Heuristic Algorithm for Privacy Preserving of Associative Classification
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Efficient Table Anonymization for Aggregate Query Answering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Closeness: A New Privacy Measure for Data Publishing
IEEE Transactions on Knowledge and Data Engineering
A new parallel association rule mining algorithm on distributed shared memory system
International Journal of Business Intelligence and Data Mining
Using multi decision tree technique to improving decision tree classifier
International Journal of Business Intelligence and Data Mining
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
Hi-index | 0.00 |
Privacy is one of the most important issues when dealing with the individual data. Typically, given a data set and a data-processing target, the privacy can be guaranteed based on the pre-specified standard by applying privacy data-transformation algorithms. Also, the utility of the data set must be considered while the transformation takes place. However, the data-transformation problem such that a privacy standard must be satisfied and the impact on the data utility must be minimised is an NP-hard problem. In this paper, we propose an approximation algorithm for the data transformation problem. The focused data processing addressed in this paper is classification using association rule, or associative classification. The proposed algorithm can transform the given data sets with O(k log k)-approximation factor with regard to the data utility comparing with the optimal solutions. The experiment results show that the algorithm is both effective and efficient comparing with the optimal algorithm and the other two heuristic algorithms.