Maximum entropy simulation for microdata protection
Statistics and Computing
A theoretical basis for perturbation methods
Statistics and Computing
A framework for privacy preserving classification in data mining
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Secure and useful data sharing
Decision Support Systems
Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data
Information Systems Research
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Data mining performance on perturbed databases: important influences on classification accuracy
International Journal of Information and Computer Security
Random orthogonal matrix masking methodology for microdata release
International Journal of Information and Computer Security
A three-layered model to implement data privacy policies
Computer Standards & Interfaces
Disclosure Analysis and Control in Statistical Databases
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
An efficient online auditing approach to limit private data disclosure
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Generating Sufficiency-based Non-Synthetic Perturbed Data
Transactions on Data Privacy
Statistical Disclosure Control for Microdata Using the R-Package sdcMicro
Transactions on Data Privacy
Sorted index numbers for privacy preserving face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
Deriving private information from arbitrarily projected data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Privacy disclosure analysis and control for 2D contingency tables containing inaccurate data
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
Disclosure analysis for two-way contingency tables
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Disclosure Control of Confidential Data by Applying Pac Learning Theory
Journal of Database Management
A decision tree-based missing value imputation technique for data pre-processing
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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The security of organizational databases has received considerable attention in the literature in recent years. This can be attributed to a simultaneous increase in the amount of data being stored in databases, the analysis of such data, and the desire to protect confidential data. Data perturbation methods are often used to protect confidential, numerical data from unauthorized queries while providing maximum access and accurate information to legitimate queries. To provide accurate information, it is desirable that perturbation does not result in a change in relationships between attributes. In the presence of nonconfidential attributes, existing methods will result in such a change. This study describes a new method (General Additive Data Perturbation) that does not change relationships between attributes. All existing methods of additive data perturbation are shown to be special cases of this method. When the database has a multivariate normal distribution, the new method provides maximum security and minimum bias. For nonnormal databases, the new method provides better security and bias performance than the multiplicative data perturbation method.