Statistical disclosure methods

  • What are the four traditional methods of statistical disclosure limitation?

    This brief overview of the literature on statistical disclosure limitation has stressed four categories of approaches: sampling, perturbation, collapsing (aggregation), and the use of synthetic data..

  • What is an example of an inferential disclosure?

    Inferential disclosure occurs when information can be inferred with high confidence from statistical properties of the released data.
    For example, the data may show a high correlation between income and purchase price of a home..

  • What is statistical confidentiality?

    Statistical confidentiality refers to the protection of information of individual statistical units and must be differentiated from other forms of confidentiality under which information is not disseminated owing to other considerations, for example national security concerns..

  • What is the statistical disclosure limitation method?

    Disclosure limitation method (also known as disclosure avoidance method) is a general term referring to a statistical technique used to manipulate the data prior to release to minimize the risk of inadvertent or unauthorized disclosure of personally identifiable information (PII)..

  • What is the statistical disclosure?

    Statistical Disclosure Control (SDC) refers to methods that allow the dissemination of statistical information while ensuring that individuals are protected against disclosure.
    The key challenge in SDC is achieving this protection while ensuring that information loss is kept to a minimum..

  • SDC seeks to treat and alter the data so that the data can be published or released without revealing the confidential information it contains, while, at the same time, limit information loss due to the anonymization of the data.
    In this guide, we discuss only disclosure control for microdata.
  • Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata.
Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata.
Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven  NecessityOutput SDCPrinciples-Based SDCCritiques

How do statistical agencies ensure a secure Microdata dissemination?

Proper and secure microdata dissemination requires statistical agencies to establish policies and procedures that formally define the conditions for accessing microdata (Dupriez and Boyko, 2010), and to apply statistical disclosure control (SDC) methods to data before release.

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What are disclosure risk measures based on?

After applying any SDC method, disclosure risk measures are based on record linkage approaches introduced in Section 3.6.
The risk measure should be compared and assessed together with information loss measures, such as:

  1. IL1s and diferences in eigenvalues introduced in Section 5
1.
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What are SDC methods and measures of disclosure risk and information loss?

The specific SDC methods and measures of disclosure risk and information loss will be explained in the following sections.
Before applying any SDC methods, the original data is assumed to have disclosure risk of 1 and information loss of 0.
As shown in Figure 1, two diferent SDC methods are applied to the same dataset.

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What is Statistical Disclosure Control (SDC) in data-driven research?

Technique used in data-driven research Statistical disclosure control(SDC), also known as statistical disclosure limitation(SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata.


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