Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining
IEEE Transactions on Knowledge and Data Engineering
Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data
Information Systems Research
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In this paper, we propose an improved pruning algorithm with memory, which we call improved EDP algorithm. This method provides the better trade-off between data quality and privacy protection against classification attacks. The proposed algorithm reduces the time complexity degree significantly, especially in the case of the complete binary tree of which worst-case time complexity is of order O(M logM), where M is the number of internal nodes of the complete tree. The experiments also show that the proposed algorithm is feasible and more efficient especially in the case of large and more complex tree structure with more internal nodes, etc. From a practical point of view, the improved EDP algorithm is more applicable and easy to implement.