A General Additive Data Perturbation Method for Database Security
Management Science
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
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
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
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
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Data Obfuscation: Anonymity and Desensitization of Usable Data Sets
IEEE Security and Privacy
Random-data perturbation techniques and privacy-preserving data mining
Knowledge and Information Systems
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
The Applicability of the Perturbation Model-based Privacy Preserving Data Mining for Real-world Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Toward privacy in public databases
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
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In this paper a novel technique useful to guarantee privacy of sensitive data with specific focus on numeric databases is presented. It is noticed that analysts and decision makers are interested in summary values of the data rather than the actual values. The proposed method considers that the maximum information lies in association of attributes rather than their actual proper values. Therefore it is aimed to perturb attribute associations in a controlled way, by shifting the data values of specific columns by rotating fields. The number of rotations is determined via using a support function for association rule handling and an algorithm that computes the best-choice rotation dynamically. Final summary statistics such as average, standard deviation of the numeric data are preserved by making bin average replacements for the actual values. The methods are tested on selected datasets and results are reported.