Data Security Center:Configure and implement data masking

Data masking methods

{
    "dataHeaderList": ["name", "age"],
    "dataList": [
        ["lily", 18],
        ["lucy", 17]
    ],
    "ruleList": [1002, null]
}

Examples of data masking results

DSC supports the following masking algorithms: hashing, redaction, substitution, rounding, encryption, and shuffling. The following tables provide examples of data masking results based on different masking algorithms.

Billing overview

Only DSC Enterprise Edition supports the data masking feature. After you purchase DSC Enterprise Edition, you can use the data masking feature. DSC uses the subscription billing method. For more information, see Billing overview. If you use the static data masking method, you may be charged additional fees.

Activate DSC

• If you do not activate DSC or only activate the free edition of DSC, you can purchase DSC Enterprise Edition. For more information, see Purchase DSC.

• If you activate the free edition of DSC and want to use the data masking feature, you must upgrade DSC to Enterprise Edition. For more information, see Specification change of subscription DSC.

Static data masking

Feature description

When you create a static data masking task, you can select a configured masking template. You can also configure masking algorithms for sensitive fields to be masked. For more information about how to configure a data masking template, see Configure data masking templates and algorithms.

Prerequisites

If you use the static data masking method to mask a database or an OSS object, you must grant DSC access permissions on the data assets to be masked. For more information, see the following topics:

• Authorize DSC to access databases

• Authorize DSC to access MaxCompute

• Authorize DSC to access a self-managed database hosted on an ECS instance

• Authorize DSC to access unstructured data in OSS and Simple Log Service

Create a data masking task

You can create a data masking task to specify the scope and rules for data masking.

1. Log on to the DSC console.

2. In the left-side navigation pane, choose Risk Governance > Data Desensitization.

3. On the Static Desensitization tab, click the Task Configurations tab. Then, click the Add Desensitization Task tab.

4. Follow the on-screen instructions to configure the parameters for a data masking task.

   1. Configure the parameters in the Basic Task Information step of the Add Desensitization Task wizard and click Next.

      Note

      You can specify a custom task name.

   2. Configure the parameters in the Desensitization Source Configuration step of the Add Desensitization Task wizard and click Next.

      ApsaraDB RDS tables, PolarDB-X tables, MaxCompute tables, PolarDB tables, ApsaraDB for OceanBase tables, AnalyticDB for MySQL tables, and self-managed database tables

      Parameter	Required	Description
      Types of data storage	Yes	The storage type of the source file for data masking. Set the parameter to ApsaraDB RDS Tables/PolarDB-X Tables/MaxCompute Tables/PolarDB Tables/ApsaraDB for OceanBase Tables/AnalyticDB for MySQL Tables/Self-managed Database Tables.
      Source Service	Yes	The service that provides the source table for data masking. Valid values: RDS, PolarDB-X, OceanBase, MaxCompute, ADB-MYSQL, PolarDB, and ECS self-built database.
      Source Database/Project	Yes	The database or project that stores the source table.
      SOURCE table name	Yes	The name of the source table.
      Source Partition	No	If you set the Source Service parameter to MaxCompute, you can configure the Source Partition parameter. The name of the partition that stores the data to mask in the source table. If you leave this parameter empty, DSC masks the sensitive data in all partitions of the source table. You can specify partitions when you create a MaxCompute table. Partitions define different logical divisions of a table. When you query data, you can specify partitions to improve query efficiency. For more information, see Partition.
      Sample SQL	No	If you set the Source Service parameter to RDS, PolarDB-X, OceanBase, or ECS self-built database, you can configure the Sample SQL parameter. The SQL statement that specifies the scope of the sensitive data to mask. If you leave this parameter empty, DSC masks all data in the source table.

      OSS objects

      Important

      Only TXT, CSV, XLSX, and XLS objects can be masked.

      Parameter	Required	Description
      Types of data storage	Yes	The storage type of the source file for data masking. Set the parameter to OSS files.
      File source	Yes	The source of the OSS object for data masking. Valid values: Uploaded Local File and OSS Bucket.
      Upload files	Yes	If you set the File source parameter to Uploaded Local File, click Select a local file to upload the OSS object.
      OSS Bucket where the source file is located	Yes	If you set the File source parameter to OSS Bucket, select the OSS bucket to which the OSS object belongs from the drop-down list. You can enter a keyword to search for and select the OSS bucket from the drop-down list.
      Source file names	Yes	If you set the File source parameter to OSS Bucket, specify the name of the source OSS object. The name must contain a file extension. If you want to mask data in an OSS object, specify the name of the OSS object. Example: test.csv. If you want to mask data in multiple OSS objects, turn on Open the pass. The system uses the same masking rule to mask multiple OSS objects. The objects must have the same format and identical column structure. After you turn on Open the pass, you can use asterisks (*) as wildcards to specify multiple OSS objects and mask data in the objects at a time. You can use asterisks (*) only in the prefix of an object name. Example: test*.xls, which matches XLS files whose name starts with test.
      Source file description	No	If you set the File source parameter to Uploaded Local File, enter a description for the source OSS object.
      Separator selection	No	The column delimiter. Select a delimiter based on the delimiter of the source OSS object. This parameter is required for CSV and TXT objects. Valid values: Semicolon ";" (MacOS/Linux default) Comma "," (Windows default) Operator '|'.
      Table contains header rows	No	Specifies whether the source OSS object contains header rows.

   3. Configure the parameters in the Desensitization algorithm step and click Next.

      

      • Select a data masking template from the drop-down list. In the source field list, the switch in the Desensitization column is automatically turned on and masking algorithms are automatically configured for fields based on the data masking template.

        The settings in the rule list of the data masking template must match the source fields that you want to mask. Otherwise, the data masking template does not take effect. For more information about how to configure a data masking template, see Configure data masking templates and algorithms.

      • Find the source field whose data you want to mask in the list, turn on the switch in the Desensitization column, and then select a masking algorithm based on your business requirements.

      You can click View and Modify Parameters in the Select Algorithm column to view and modify the rule of the selected algorithm. For more information about partition formats in algorithm rules, see Partition formats.

      Note

      If you turn on Forcefully Enable Template, you cannot change the algorithm on the page. To change the algorithm, you must modify the relevant template rule.

      Partition formats

      Partition type	Partition format	Example
      N weeks later	Custom partition key column=$[yyyymmdd+7*N]	time=$[20190710+7*1]. This partition indicates that DSC masks the data that is generated within one week after July 10, 2019.
      N weeks before	Custom partition key column=$[yyyymmdd-7*N]	time=$[20190710-7*3]. This partition indicates that DSC masks the data that is generated within three weeks before July 10, 2019.
      N days later	Custom partition key column=$[yyyymmdd+N]	time=$[20190710+2]. This partition indicates that DSC masks the data that is generated within two days after July 10, 2019.
      N days before	Custom partition key column=$[yyyymmdd-N]	time=$[20190710-5]. This partition indicates that DSC masks the data that is generated within five days before July 10, 2019.
      N hours later	Custom partition key column=$[hh24mi:ss+N/24]	time=$[0924mi:ss+2/24]. This partition indicates that DSC masks the data that is generated within 2 hours after 09:00:00 based on a 24-hour clock.
      N hours before	Custom partition key column=$[hh24mi:ss-N/24]	time=$[0924mi:ss-1/24]. This partition indicates that DSC masks the data that is generated within 1 hour before 09:00:00 based on a 24-hour clock.
      N minutes later	Custom partition key column=$[hh24mi:ss+N/24/60]	time=$[0924mi:ss+2/24/60]. This partition indicates that DSC masks the data that is generated within 2 minutes after 09:00:00 based on a 24-hour clock.
      N minutes before	Custom partition key column=$[hh24mi:ss-N/24/60]	time=$[0924mi:ss-2/24/60]. This partition indicates that DSC masks the data that is generated within 2 minutes before 09:00:00 based on a 24-hour clock.

5. Configure the destination where you want to store masked data and click Test below Write Permission Test. After the test is passed, click Next.

   Important

   The account that is used to connect to the data asset must have the write permissions on the data asset.

   

6. Configure the processing logic.

   Parameter	Required	Description
   How the task is triggered	Yes	The method that is used to run the data masking task. Valid values: Manual Only: If you select this option, you must manually run the data masking task. Scheduled Only: If you select this option, you must configure automatic running of the data masking task. After the configuration is complete, the data masking task is automatically run at a specific point in time on an hourly, daily, weekly, or monthly basis. Manual + Scheduled: If you select this option, you can click Start in the Actions column on the Task Configurations tab to manually run the data masking task. You can also configure automatic running of the data masking task. After the configuration is complete, the data masking task is automatically run at a specific point in time on an hourly, daily, weekly, or monthly basis.
   Turn on incremental desensitization	No	Specifies whether to enable incremental masking. If you turn on this switch, DSC masks only the data that is added after the previous data masking task is complete. You must specify a field whose value is increased over time as the incremental identifier. For example, you can specify the creation time field or the auto-increment ID field as the incremental identifier. Important DSC supports incremental data masking only for ApsaraDB RDS databases.
   Shard field	No	The shard field based on which DSC divides the source data into multiple shards. DSC concurrently masks the source data in the shards to improve the efficiency of data masking. You can specify one or more shard fields based on your business requirements. DSC supports incremental data masking only for ApsaraDB RDS databases. We recommend that you use a primary key or a field on which a unique index is created as the shard field. If you leave this parameter empty, a primary key is used as the shard field. DSC divides the source data based on the primary key and masks the source data. Important If the source data does not have a primary key, you must specify a shard field. Otherwise, the data masking task fails. If you specify excessive shard fields, query performance and data accuracy may deteriorate. Proceed with caution.
   Table name conflict resolution	Yes	The method that is used to handle a table name conflict. Valid value: Delete the target table and create a new table with the same name. Attach data to the target table. We recommend that you select this option.
   Row Conflict Resolution	Yes	The method that is used to handle row conflicts. Valid value: Keep conflicting rows in the target table and discard the new data. We recommend that you select this option. Delete conflicting rows in the target table and insert the new data.

7. Click Submit.

Run and view a data masking task

If you set the How the task is triggered parameter to Manual Only, you must manually run the data masking task. If you set the How the task is triggered parameter to Scheduled Only, you must configure automatic running of the data masking task. After the configuration is complete, the data masking task is automatically run at a specific point in time. If you set the How the task is triggered parameter to Manual + Scheduled, you can manually run the data masking task or configure settings to automatically run the data masking task.

1. On the Static Desensitization tab, click the Task Configurations tab. Then, find the created data masking task and click Start in the Actions column to run the data masking task.

   

2. On the Static Desensitization tab, click the Status tab to view the progress and status of the data masking task.

   

Troubleshoot the errors that occur in a data masking task

If a data masking task fails, you can refer to the following table to troubleshoot the errors.

Modify or delete a data masking task

You cannot modify or delete a data masking task that is pending execution or is running.

• Modify a data masking task

  If you want to modify the settings of a data masking task, find the task and click Modify in the Actions column.

• Delete a data masking task

  Important

  After a data masking task is deleted, it cannot be restored. Proceed with caution.

  If you no longer use a data masking task, you can delete the task. Find the task and click Delete in the Actions column. In the message that appears, click OK.

Dynamic data masking

Feature description

Dynamic data masking tasks rely on configured masking templates to mask the specified data. You can call the ExecDatamask operation to specify the data (Data) that you want to mask and the ID of the masking template (TemplateId ). Then, the data specified in the dataList field of the Data parameter is masked based on the matching mode (field and sensitive type) defined in the masking template.

You can obtain the template ID on the Masking Configurations tab of the Data Governance > Data Desensitization page in the DSC console. You can also create a custom data masking template. For more information, see Configure data masking templates and algorithms.

The following table describes the masking methods in the masking template that is used when you call the ExecDatamask operation to mask data.

Example

Limits

Before you can call the ExecDatamask operation to dynamically mask sensitive data, make sure that the size of the sensitive data that you want to mask is less than 2 MB.

View the call records of the ExecDatamask operation

1. Log on to the DSC console.

2. In the left-side navigation pane, choose Risk Governance > Data Desensitization.

3. On the Data Desensitization page, click the Dynamic desensitization tab.

4. On the Dynamic desensitization tab, view the call records of the ExecDatamask operation.

   Note

   If you use the same account and IP address to call the ExecDatamask operation multiple times, only one record is retained. The cumulative number of calls is recorded.

Example on how to share masked data based on the static data masking method

DSC provides a static data masking method that can be used to mask sensitive data in structured CSV files in an OSS bucket. You can store the masked data in a specific OSS bucket within the same account for secure data sharing. For more information, see Mask sensitive data in OSS table files.