Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Randomization in privacy preserving data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On Privacy-Preserving Access to Distributed Heterogeneous Healthcare Information
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 6 - Volume 6
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
A Framework for Evaluating Privacy Preserving Data Mining Algorithms*
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving mining by rotational data transformation
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Performance measurements for privacy preserving data mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Confidentiality issues for medical data miners
Artificial Intelligence in Medicine
Testing terrorism theory with data mining
International Journal of Data Analysis Techniques and Strategies
Forecasting using rules extracted from privacy preservation neural network
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
International Journal of Information Systems and Social Change
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Today, the data related to business, finance and healthcare pose problems for Privacy-Preserving Data Mining (PPDM). Privacy regulations and concerns prevent data owners from sharing data for mining purposes. To circumvent this problem, data owners must design strategies to meet privacy requirements and ensure valid data mining results. This paper proposes the hybridisation of the random projection and random rotation methods for privacy-preserving classification. The hybrid method is tested on six benchmark data sets and four bank bankruptcy data sets. These methods ensure the privacy and secrecy of bank data and the resulting data set is mined without a considerable loss of accuracy. A multilayer perceptron, decision tree J48 and logistic regression are used as classifiers. The results of a tenfold cross-validation and t-test indicate improved average accuracies for the hybrid privacy preservation method compared to when random projection is used alone. The reasons for the superior performance of the hybrid privacy preservation method are also highlighted.