A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
ACM Transactions on Database Systems (TODS)
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
Correlations and Copulas for Decision and Risk Analysis
Management Science
A General Additive Data Perturbation Method for Database Security
Management Science
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Model Based Disclosure Protection
Inference Control in Statistical Databases, From Theory to Practice
The Security of Confidential Numerical Data in Databases
Information Systems Research
The use of added error to avoid disclosure in microdata releases
The use of added error to avoid disclosure in microdata releases
Spatial and non-spatial model-based protection procedures for the release of business microdata
Statistics and Computing
Information preserving statistical obfuscation
Statistics and Computing
Perturbing Nonnormal Confidential Attributes: The Copula Approach
Management Science
Maximum entropy simulation for microdata protection
Statistics and Computing
Information preserving statistical obfuscation
Statistics and Computing
Model Diagnostics for Remote Access Regression Servers
Statistics and Computing
Statistical confidentiality: Optimization techniques to protect tables
Computers and Operations Research
Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
Generating Sufficiency-based Non-Synthetic Perturbed Data
Transactions on Data Privacy
Distribution-preserving statistical disclosure limitation
Computational Statistics & Data Analysis
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
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
Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
VICUS: a noise addition technique for categorical data
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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In this paper we discuss a new theoretical basis for perturbation methods. In developing this new theoretical basis, we define the ideal measures of data utility and disclosure risk. Maximum data utility is achieved when the statistical characteristics of the perturbed data are the same as that of the original data. Disclosure risk is minimized if providing users with microdata access does not result in any additional information. We show that when the perturbed values of the confidential variables are generated as independent realizations from the distribution of the confidential variables conditioned on the non-confidential variables, they satisfy the data utility and disclosure risk requirements. We also discuss the relationship between the theoretical basis and some commonly used methods for generating perturbed values of confidential numerical variables.