Combining statistical disclosure limitation methods to preserve relationships and data-specific constraints in survey data.

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• Anna Oganian (None / Other)
• Wednesday 07 December 2016, 11:30-12:00
• Seminar Room 1, Newton Institute.

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DLAW03 - New developments in data privacy

Applications of data swapping and noise are among the most widely used methods for Statistical Disclosure Limitation (SDL) by statistical agencies for public-use non-interactive data release. The core ideas of swapping and noise are conceptually easy to understand and are naturally suited for masking purposes. We believe that they are worth revisiting with a special emphasis given to the utility aspects of these methods and to the ways of combining the methods to increase their efficiency and reliability.  Indeed, many data collecting agencies use complex sample designs to increase the precision of their estimates and often allocate additional funds to obtain larger samples for particular groups in the population. Thus, it is particularly undesirable and counterproductive when SDL methods applied to these data significantly change the magnitude of estimates and/or their levels of precision. We will present and discuss two methods of disclosure limitation based on swapping and noise, which can work together in synergy while protecting continuous and categorical variables. The first method is a version of multiplicative noise that preserves means and covariance together with some structural constraints in the data. The second method is loosely based on swapping. It is designed with the goal of preserving the relationships between strata-defining variables with other variables in the survey. We will show how these methods can be applied together enhancing each other’s efficiency.

This talk is part of the Isaac Newton Institute Seminar Series series.

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