Disclosure control and data dissemination

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  • Articles and reports: 11-522-X200600110431
    Description:

    We describe statistical disclosure control methods (SDC) developed for a public release Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) micro-data file. CHIRPP is a national injury surveillance database managed by the Public Health Agency of Canada (PHAC). After describing CHIRPP, the paper includes a brief overview of basic SDC concepts, as an introduction to the process for selecting and developing the appropriate SDC methods for CHIRPP given its specific challenges and requirements. We then summarize some key results. The paper concludes with a discussion of the implication of this work for the health information field and closing remarks with respect to the some methodological issues for consideration.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110433
    Description:

    The process of public-use micro-data files creation involves a number of components. One of its key elements is RTI International's innovative MASSC methodology. However, there are other major components in this process such as treatment of non-core identifying variables and extreme outcomes for extra protection. The statistical disclosure limitation is designed to counter both inside and outside intrusion. The components of the process are accordingly designed.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110434
    Description:

    Protecting respondents from disclosure of their identity in publicly released survey data is of practical concern to many government agencies. Methods for doing so include suppression of cluster and stratum identifiers and altering or swapping record values between respondents. Unfortunately, stratum and cluster identifiers are usually needed for variance estimation using linearization and for replication methods as resampling is typically done on first-stage sampling units within strata. One might feel that releasing a set of replicate weights that also have stratum and cluster identifiers suppressed might circumvent this problem to some extent, especially using some random resampling such as the bootstrap. In this article, we first demonstrate that by viewing the replicate weights as observations in a high dimensional space one can easily use clustering algorithms to reconstruct the cluster identifiers irrespective of the resampling method even if the resampling weights are randomly altered. We then propose a fast algorithm for swapping cluster and strata identifiers of ultimate units before creating replicate weights without significantly impacting resulting variance estimates of characteristics of interest. The methods are illustrated by application to publicly released data from the National Health and Nutrition Examination Surveys, where such disclosure issues are extremely important..

    Release date: 2008-03-17
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  • Articles and reports: 11-522-X200600110431
    Description:

    We describe statistical disclosure control methods (SDC) developed for a public release Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) micro-data file. CHIRPP is a national injury surveillance database managed by the Public Health Agency of Canada (PHAC). After describing CHIRPP, the paper includes a brief overview of basic SDC concepts, as an introduction to the process for selecting and developing the appropriate SDC methods for CHIRPP given its specific challenges and requirements. We then summarize some key results. The paper concludes with a discussion of the implication of this work for the health information field and closing remarks with respect to the some methodological issues for consideration.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110433
    Description:

    The process of public-use micro-data files creation involves a number of components. One of its key elements is RTI International's innovative MASSC methodology. However, there are other major components in this process such as treatment of non-core identifying variables and extreme outcomes for extra protection. The statistical disclosure limitation is designed to counter both inside and outside intrusion. The components of the process are accordingly designed.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110434
    Description:

    Protecting respondents from disclosure of their identity in publicly released survey data is of practical concern to many government agencies. Methods for doing so include suppression of cluster and stratum identifiers and altering or swapping record values between respondents. Unfortunately, stratum and cluster identifiers are usually needed for variance estimation using linearization and for replication methods as resampling is typically done on first-stage sampling units within strata. One might feel that releasing a set of replicate weights that also have stratum and cluster identifiers suppressed might circumvent this problem to some extent, especially using some random resampling such as the bootstrap. In this article, we first demonstrate that by viewing the replicate weights as observations in a high dimensional space one can easily use clustering algorithms to reconstruct the cluster identifiers irrespective of the resampling method even if the resampling weights are randomly altered. We then propose a fast algorithm for swapping cluster and strata identifiers of ultimate units before creating replicate weights without significantly impacting resulting variance estimates of characteristics of interest. The methods are illustrated by application to publicly released data from the National Health and Nutrition Examination Surveys, where such disclosure issues are extremely important..

    Release date: 2008-03-17
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