Creating a SQL analysis template - AWS Clean Rooms

Creating a SQL analysis template

Prerequisites

Before you create a SQL analysis template, you must have:

  • An active AWS Clean Rooms collaboration

  • Access to at least one configured table in the collaboration

    For information about configuring tables in AWS Clean Rooms, see Creating a configured table in AWS Clean Rooms.

  • Permissions to create analysis templates

  • Basic knowledge of SQL query syntax

The following procedure describes the process of creating a SQL analysis template using the AWS Clean Rooms console.

For information about how to create a SQL analysis template using the AWS SDKs, see the AWS Clean Rooms API Reference.

To create a SQL analysis template

  1. Sign in to the AWS Management Console and open the AWS Clean Rooms console with the AWS account that will function as the collaboration creator.

  2. In the left navigation pane, choose Collaborations.

  3. Choose the collaboration.

  4. On the Templates tab, go to the Analysis templates created by you section.

  5. Choose Create analysis template.

  6. On the Create analysis template page, for Details,

    1. Enter a Name for the analysis template.

    2. (Optional) Enter a Description.

    3. For Format, leave the SQL option selected.

  7. For Tables, view the configured tables associated with the collaboration.

  8. For Definition,

    1. Enter the definition for the analysis template.

    2. Choose Import from to import a definition.

    3. (Optional) Specify a parameter in the SQL editor by entering a colon (:) in front of the parameter name.

      For example:

      WHERE table1.date + :date_period > table1.date

  9. If you added parameters previously, under Parameters – optional, for each Parameter name, choose the Type and Default value (optional).

  10. For Synthetic data, if you want to generate synthetic data for model training, select the Require analysis template output to be synthetic checkbox.

    For more information, see Privacy-enhanced synthetic dataset generation.

    1. For Column classification, choose a Column from the dropdown list. At least five columns are required.

      1. Choose a Classification from the dropdown list. This identifies the data type for each column.

        Classification types include:

        • Numerical – Continuous numerical values such as measurements or counts

        • Categorical – Discrete values or categories such as labels or types

      2. To remove a column, select Remove.

      3. To add another column, select Add another column. Choose the Column and Classification from the dropdown lists.

      4. For Predictive value, choose a Column from the dropdown list. This is the column the custom model uses for prediction after it's trained on the synthetic dataset.

    2. Advanced settings allow you to set the Privacy level and Privacy threshold. Adjust the settings to fit your needs.

      1. For Privacy level, enter an epsilon value to determine how much noise the synthetic model adds to protect privacy in your generated dataset. The value must be between 0.0001 and 10.

        • Lower values add more noise, providing stronger privacy protection but potentially reducing utility for downstream custom model trained on this data.

        • Higher values add less noise, providing more accuracy but potentially reducing privacy protection.

        For Privacy threshold, enter the highest allowed probability that a membership inference attack could identify members of the original dataset. The value must be between 50.0 and 100.

        • Scores of 50% indicate that a membership inference attack can't successfully distinguish members from non-members better than a random guess.

        • For no privacy limit, enter 100%.

        The optimal value depends on your specific use case and privacy requirements. If the privacy threshold is exceeded, the ML input channel creation fails, and you can't use the synthetic dataset to train a model.

    Warning

    Synthetic data generation protects against inferring individual attributes whether specific individuals are present in the original dataset or learning attributes of those individuals are present. However, it doesn't prevent literal values from the original dataset, including personally identifiable information (PII) from appearing in the synthetic dataset.

    We recommend avoiding values in the input dataset that are associated with only one data subject because these may re-identify a data subject. For example, if only one user lives in a zip code, the presence of that zip code in the synthetic dataset would confirm that user was in the original dataset. Techniques like truncating high precision values or replacing uncommon catalogues with other can be used to mitigate this risk. These transformations can be part of the query used to create the ML input channel.

  11. If you want to enable Tags for the resource, choose Add new tag and then enter the Key and Value pair.

  12. Choose Create.

  13. You are now ready to inform your collaboration member that they can Review an analysis template. (Optional if you want to query your own data.)