Lakehouse Column-Level Security (Dynamic Masking) User Guide
Overview
Column-level Security provides fine-grained data protection capabilities through Dynamic Data Masking, which dynamically modifies the display of sensitive data (such as partial hiding or character replacement) based on user identity or role. The system only stores the original data and executes the masking function at runtime during data access. This document introduces how to implement this functionality through the SQL interface.
Core Syntax
1 Creating Masking Policy Functions
Refer to the CREATE FUNCTION (SQL) syntax.
CREATE FUNCTION [schema_name.]function_name (col_name column_type)
RETURNS output_type
AS
expression_with_conditional_logic;
Key Elements:
- Must return the same data type as the original column.
- Use security context functions:
current_user() to get the current user (note case sensitivity).current_roles() to get an array of user roles.
2 Binding Policies to Columns
When Creating a Table:
CREATE TABLE table_name (
col1 STRING MASK schema_name.masking_function,
...
);
Modifying an Existing Table:
ALTER TABLE table_name
CHANGE COLUMN column_name
SET MASK schema_name.masking_function;
Adding a Column with Masking:
ALTER TABLE table_name ADD COLUMN (column_name column_type MASK schema_name.masking_function);
3 Removing Policy Binding
ALTER TABLE table_name
CHANGE COLUMN column_name
UNSET MASK;
Use Case Examples
1 Basic Masking
Requirement: Display the first 6 characters of an ID card number, followed by 4 asterisks, and then the last 4 characters.
CREATE FUNCTION public.idcard_masking(idcard STRING)
RETURNS STRING
AS concat(substr(idcard, 1, 6), repeat('*', 4), substr(idcard, 10, 4));
ALTER TABLE data CHANGE COLUMN idcard SET MASK public.idcard_masking;
Query Effect:
Original Value: 130183199901011234 → Masked: 130183****9010
2 Dynamic Masking Based on User
Requirement: Only the UAT_TEST user should see masked data.
CREATE FUNCTION public.idcard_masking(idcard STRING)
RETURNS STRING
AS
CASE
WHEN current_user() = "UAT_TEST"
THEN concat(substr(idcard, 1, 6), repeat('*', 4), substr(idcard, 10, 4))
ELSE idcard
END;
-- Ignoring case sensitivity of the username
CREATE FUNCTION public.idcard_masking(idcard STRING)
RETURNS STRING
AS
CASE
WHEN lower(current_user()) = "uat_test"
THEN concat(substr(idcard, 1, 6), repeat('*', 4), substr(idcard, 10, 4))
ELSE idcard
END;
3 Dynamic Masking Based on Role
Requirement: Users with the user_admin role can view the full information.
CREATE FUNCTION public.idcard_masking_role(idcard STRING)
RETURNS STRING
AS
CASE
WHEN array_contains(current_roles(), "user_admin")
THEN idcard
ELSE concat(substr(idcard,1,6), '****', substr(idcard,11,4))
END;
Complete Operation Example
1 Initializing the Environment
CREATE SCHEMA IF NOT EXISTS security_demo;
USE security_demo;
-- General masking function
CREATE FUNCTION security_demo.ssn_mask(ssn STRING)
RETURNS STRING
AS concat('***-**-', substr(ssn, 8, 4));
CREATE TABLE security_demo.user_data (
name STRING,
ssn STRING MASK security_demo.ssn_mask, -- Binding directly when creating the table
phone STRING
);
INSERT INTO security_demo.user_data VALUES ('James', '123-45-6789', '123456789');
SELECT * FROM security_demo.user_data;
2 Creating Policy Functions
-- Exemption for privileged roles
CREATE FUNCTION security_demo.admin_ssn_mask(ssn STRING)
RETURNS STRING
AS
CASE
WHEN array_contains(current_roles(), 'user_admin') THEN ssn
ELSE concat('***-**-', substr(ssn,8,4))
END;
3 Modifying Masking Policies
-- Removing the previous policy
ALTER TABLE security_demo.user_data CHANGE COLUMN ssn UNSET MASK;
-- Adding a new policy
ALTER TABLE security_demo.user_data CHANGE COLUMN ssn SET MASK security_demo.admin_ssn_mask;
4 Verifying the Effect
Query by a Regular User:
SELECT * FROM user_data;
-- Output: John Doe ***-**-6789 138****1234
Query by a USER_ADMIN Role:
SELECT * FROM user_data;
-- Output: John Doe 123-45-6789 138****1234
Management Notes
1 Permission Control
- Only roles with
ALTER TABLE permissions are allowed to modify masking policies.
- Function creation requires
CREATE FUNCTION permissions.
- Avoid using complex calculations in masking functions.
- Use conditional logic cautiously for columns with high query frequency.
Limitations
- Only one masking policy can be bound to a single column. If you want to define multiple masking rules, you can use conditional logic within a single function to apply different policies.
