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The problem

Masking Numerical Microdata – Krish Muralidhar. The problem. The techniques. Noise Based* Additive noise Multiplicative noise Kim’s Method PRAM Multiple Imputation Information Preserving Statistical Obfuscation Non-noise Based Univariate microaggregation Multivariate microaggregation

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The problem

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  1. Masking Numerical Microdata – Krish Muralidhar The problem The techniques Noise Based* Additive noise Multiplicative noise Kim’s Method PRAM Multiple Imputation Information Preserving Statistical Obfuscation Non-noise Based Univariate microaggregation Multivariate microaggregation Swapping Hybrid Approaches Shuffling *Noise based approaches such as Model based and GADP are not considered since they are subsets of IPSO • Mask numerical microdata • Data set consists of a set of N records with K categorical non-confidential variables, L non-confidential numerical variables, M confidential numerical variables • Objectives • Disclosure Risk • Data Utility • Univariate distribution • Multivariate distribution • Covariance Matrix • Correlation (Product moment, Rank order, Other) • Inference (Parametric, Rank based, Other) • Ease of Use • Ease of Implementation

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