The present paper proposes a method to reduce the random space based on both stochastic criteria and structural performance. The high number of random variables required to model a random field often inhibits accurate probabilistic analyses based on Monte Carlo methods. These can be modelled as random fields for the purpose of robust optimization or reliability analysis. 1Īny mechanical or civil engineering structure possesses some natural randomness in its properties which fluctuates over space. We conclude the chapter with observations on measures for assessing disclosure risk and information loss brought by the application of protection techniques. We survey the main techniques that have been proposed to protect microdata from improper disclosure by distinguishing them in masking techniques (which protect data by masking or perturbing their values), and synthetic data generation techniques (which protect data by replacing them with plausible, but made up, values). In this chapter, we first characterize the microdata protection problem (in contrast to macrodata protection), discussing the disclosure risks at which microdata are exposed. This has created an increasing demand on organizations to devote resources for adequate protection of microdata. The protection of microdata against improper disclosure is therefore an issue that has become increasingly important and will continue to be so. Most data are today released in the form of microdata, reporting information on individual respondents.
Governmental, public, and private organizations are more and more frequently required to make data available for external release in a selective and secure fashion.
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