Generating Sufficiency-based Non-Synthetic Perturbed Data

The mean vector and covariance matrix are sufficient statistics when the un derlying distribution is multivariate normal. Many type of statistical analyses used in practice rely on the assumption of multivariate normality (Gaussian model). For these analyses, maintaining the mean vector and covariance matrix of the masked data to be the same as that of the original data implies that if the masked data is analyzed using these techniques, the results of such analysis will be the same as that using the original data. For numerical confidential data, a recently proposed perturbation method makes it possible to maintain the mean vector and covariance matrix of the masked data to be exactly the same as the original data. However, as it is currently proposed, the per turbed values from this method are considered synthetic because they are generated without considering the values of the confidential variables (and are based only on the nonconfidential variables). Some ...

• Type: Journal
• Year: 2008
• Where: TDP
• Authors: Krishnamurty Muralidhar, Rathindra Sarathy
• Keywords: Covariance Matrix, Mean Vector, Original Data, TDP 2008