🔐 Securing Sensitive Data in GBase Database: From Masking to Policy Enforcement

🚨 The Hidden Risk in Modern Databases

Applications today store massive amounts of sensitive data:

Emails
Phone numbers
Identity numbers

But many systems still rely on:

Backend masking logic
Inconsistent security rules

This creates serious risks.

👉 GBase database solves this at the database level.

🧠 Built-in Data Masking in GBase

GBase provides native masking functions, allowing data to be protected automatically during queries.

Example function:

keymask(substr, padding, pos)
🛠️ Example: Mask Email Data
Create Table
CREATE TABLE users (
email VARCHAR(255)
MASKED WITH (FUNCTION = 'keymask("@", "****", 0)')
);
Insert Data
INSERT INTO users VALUES ('[e-mail]');
Query Result
Role Output
Admin [e-mail]

Analyst ****@gbase.cn
🔄 Combine Masking with Roles
CREATE ROLE analyst;
GRANT SELECT ON users TO analyst;

👉 Masking is automatically applied based on permissions.

⚡ Why Database-Level Security Wins
Approach Advantage
App-level masking Flexible but inconsistent
GBase masking Centralized and automatic
🔍 Bonus: Works with Distributed Queries

Even in distributed execution:

Masking still applies
No extra network cost
No application changes needed
🚀 Final Thoughts

GBase turns security into a database responsibility, not a developer burden.

👉 Less code
👉 More security
👉 Better consistency