A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
Mining the Web: Transforming Customer Data into Customer Value
Mining the Web: Transforming Customer Data into Customer Value
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Information preserving statistical obfuscation
Statistics and Computing
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Johnny 2: a user test of key continuity management with S/MIME and Outlook Express
SOUPS '05 Proceedings of the 2005 symposium on Usable privacy and security
Secrecy, flagging, and paranoia: adoption criteria in encrypted email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Generalized Additive Models (Texts in Statistical Science)
Generalized Additive Models (Texts in Statistical Science)
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data
Information Systems Research
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Why Johnny can't encrypt: a usability evaluation of PGP 5.0
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
Myths and fallacies of "Personally Identifiable Information"
Communications of the ACM
Design science in information systems research
MIS Quarterly
Why swap when you can shuffle? a comparison of the proximity swap and data shuffle for numeric data
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
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Business organizations are generating growing volumes of data about their employees, customers, and suppliers. Much of these data cannot be exploited for business value due to privacy and confidentiality concerns. National statistical agencies share sensitive data collected from individuals and businesses by modifying the data so individuals and firms cannot be identified but statistical utility is preserved. We build on this literature to develop a hybrid approach to data masking for business organizations. We demonstrate the validity of the hybrid approach, which we call multiple imputation with multimodal perturbation (MIMP), using Monte Carlo simulation and illustrate its application in a specific business context. Results of our analysis open new areas of research for information systems scholarship and new potential revenue sources for business organizations.