🔐 Building Secure Applications with GBase Database: Dynamic Data Masking in Practice

🧩 The Real Problem

In many systems, sensitive data protection is implemented in application code:

• Emails masked in backend
• Phone numbers hidden in frontend
• Different logic across services

This leads to inconsistency and security risks.

---

💡 GBase Solution: Database-Level Masking

GBase database provides dynamic data masking, allowing you to control how data is displayed without modifying stored values.

👉 Masking happens at query time
👉 Controlled by permissions
👉 No code changes required

According to GBase design, masking only affects returned results, not stored data ([GBase][1])

---

⚙️ Example: Email Masking

Step 1: Create Table

CREATE TABLE users (
    id INT,
    email VARCHAR(255)
    MASKED WITH (FUNCTION = 'keymask("@", "****", 0)')
);

---

Step 2: Insert Data

INSERT INTO users VALUES (1, '[e-mail]');

---

Step 3: Query Behavior

Role	Result
Admin	[e-mail]
Analyst	****@gbase.cn

---

🧠 How It Works

GBase supports multiple masking strategies:

• Partial masking
• Random masking
• Hash masking
• keymask function

Masking is applied based on user permissions ([GBase][1])

---

🔐 Why This Is Powerful

✅ Centralized Security

All rules live inside the database.

✅ Consistent Enforcement

Same behavior across:

• APIs
• BI tools
• Direct SQL

✅ Reduced Code Complexity

No need for custom masking logic.

---

🏢 Real Use Cases

• Banking → hide account numbers
• Healthcare → protect patient IDs
• Analytics → safe data sharing

---

🚀 Final Thoughts

With GBase, security becomes part of your data layer, not an afterthought.

👉 Write less code
👉 Reduce risk
👉 Enforce policies automatically