Disclosure control and data dissemination

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  • Articles and reports: 12-001-X201700114818
    Description:

    The protection of data confidentiality in tables of magnitude can become extremely difficult when working in a custom tabulation environment. A relatively simple solution consists of perturbing the underlying microdata beforehand, but the negative impact on the accuracy of aggregates can be too high. A perturbative method is proposed that aims to better balance the needs of data protection and data accuracy in such an environment. The method works by processing the data in each cell in layers, applying higher levels of perturbation for the largest values and little or no perturbation for the smallest ones. The method is primarily aimed at protecting personal data, which tend to be less skewed than business data.

    Release date: 2017-06-22
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  • Articles and reports: 12-001-X201700114818
    Description:

    The protection of data confidentiality in tables of magnitude can become extremely difficult when working in a custom tabulation environment. A relatively simple solution consists of perturbing the underlying microdata beforehand, but the negative impact on the accuracy of aggregates can be too high. A perturbative method is proposed that aims to better balance the needs of data protection and data accuracy in such an environment. The method works by processing the data in each cell in layers, applying higher levels of perturbation for the largest values and little or no perturbation for the smallest ones. The method is primarily aimed at protecting personal data, which tend to be less skewed than business data.

    Release date: 2017-06-22
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