Security of random data perturbation methods
ACM Transactions on Database Systems (TODS)
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
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The VLDB Journal — The International Journal on Very Large Data Bases
Singular value decomposition based data distortion strategy for privacy protection
Knowledge and Information Systems
Privacy-preserving mining by rotational data transformation
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
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Churn prediction is an important component of customer retention to predict whether a current customer decides to take business elsewhere or voluntarily terminates service, so marketing campaigns can target at the potential churners for retention efforts. In this paper we provide a strategy to protect customers' privacy in churn prediction. First of all, we demonstrate how to use data distortion to mask a telecom customer dataset, and then apply churn prediction methods to the distorted data. Since the distorted data are so different from the original data the privacy of customer is preserved, but the prediction methods we proposed will not compromise the accuracy of churn prediction. The performance of several data distortion methods are compared and evaluated.