Comprehensive Guide to Data Security and Data Masking in ClickHouse: Protecting Your Data While Maintaining Performance

In today’s data-driven landscape, organizations face the dual challenge of leveraging their data for insights while ensuring robust protection of sensitive information. For companies using ClickHouse, an open-source columnar database management system known for its exceptional query performance, implementing proper security measures is crucial. This blog explores the comprehensive approaches to data security and masking in ClickHouse, based on a detailed presentation by Shiv Iyer

The importance of data security cannot be overstated – from meeting regulatory requirements like GDPR and HIPAA to preventing costly data breaches that damage both finances and reputation. This guide walks through the essential security features in ClickHouse and provides practical implementation advice for database administrators and data engineers

Understanding ClickHouse Security Fundamentals

Security in ClickHouse starts with proper user management and access control. These foundational elements create the first line of defense against unauthorized data access.

User Management and Access Control

ClickHouse provides robust mechanisms for controlling who can access your data and what they can do with it. The platform follows the principle of least privilege, ensuring users only have access to the specific data they need for their roles.

Creating users with specific privileges forms the cornerstone of ClickHouse security:

CREATE USER analyst
IDENTIFIED WITH sha256_password
BY 'strong_password'
SETTINGS max_memory_usage = 10000000000;

GRANT SELECT ON database.table TO analyst;
GRANT SELECT(id, name) ON database.sensitive_table TO analyst;

Best practices for user management include following the principle of least privilege, regular auditing of user privileges, implementing strong password policies, removing unused accounts promptly, and documenting access grants for compliance purposes.

Role-Based Access Control (RBAC)

Rather than managing permissions for each individual user, RBAC allows administrators to create roles that represent job functions and assign appropriate permissions to these roles. This simplifies administration and ensures consistency in permission assignment.

The implementation involves creating roles, assigning privileges to those roles, and then linking users to the appropriate roles:

-- Create roles
CREATE ROLE analyst_role;
CREATE ROLE admin_role;

-- Grant permissions to roles
GRANT SELECT ON analytics.* TO analyst_role;
GRANT ALL ON *.* TO admin_role;

-- Assign roles to users
GRANT analyst_role TO user1;
GRANT admin_role TO admin_user;

RBAC can be further refined with role hierarchies, allowing for more nuanced permission structures within an organization.

Row-Level Security Policies

Row-level security adds another dimension of access control by filtering data at the row level based on user context. This powerful feature enables organizations to maintain a unified data model while controlling exactly which rows each user can see.

Implementation example:

-- Create row policy for regional access
CREATE ROW POLICY regional_access
ON sales.orders
FOR SELECT
USING region_id = currentRegion();

-- Policy for different roles
CREATE ROW POLICY manager_access
ON employees
FOR SELECT
USING (
    hasRole('manager') AND
    department_id = currentDepartmentId()
) OR hasRole('admin');

Key considerations when implementing row-level security include defining policies based on business rules, using user context variables, combining multiple conditions for complex access patterns, and thorough testing to avoid unintended restrictions.

Authentication Methods in ClickHouse

Authentication validates user identities and serves as the entry point to the system. ClickHouse supports multiple authentication methods to suit different organizational needs.

Password Authentication

The most basic form of authentication, ClickHouse supports several password mechanisms with varying security levels:

-- Plain password (less secure)
CREATE USER user1 IDENTIFIED WITH plaintext_password
BY 'password123';

-- SHA-256 hashed password (more secure)
CREATE USER user2 IDENTIFIED WITH sha256_password
BY 'securePassword!';

-- Double SHA-1 (legacy)
CREATE USER user3 IDENTIFIED WITH
double_sha1_password BY 'anotherPassword';

For production environments, SHA-256 is recommended due to its stronger security profile.

SSL Certificate Authentication

For environments requiring stronger security, certificate-based authentication eliminates the need for password transmission. The process involves generating SSL certificates, configuring the server to validate client certificates, securely distributing certificates, and setting up clients to present certificates when connecting.

LDAP Integration

Organizations with existing directory services can benefit from LDAP integration, which provides centralized user management, simplified authentication, integration with enterprise systems, enforcement of password policies, and reduced administrative overhead.

Data Masking Techniques

Data masking hides original data with modified content while preserving data format and usability for non-sensitive purposes. This is particularly valuable for protecting personal information while allowing access for development, testing, or analysis.

Built-in SQL Functions

ClickHouse provides several built-in functions designed specifically for data masking:

• maskPhone() – Masks phone numbers, keeping country code and last 4 digits
• maskEmail() – Masks email addresses, keeping first letter and domain
• maskCardNumber() – Masks credit card numbers, keeping only last 4 digits
• maskData() – General purpose masking with configurable behavior

Example implementation:

SELECT
    id,
    name,
    maskPhone(phone_number) AS masked_phone,
    maskEmail(email) AS masked_email,
    maskCardNumber(credit_card) AS masked_cc
FROM customers;

Custom User-Defined Functions

For unique masking requirements, ClickHouse allows creation of custom masking functions:

CREATE FUNCTION maskCustomData AS
(input, showChars) ->
if(
    length(input) <= showChars,
    input,
    substring(input, 1, showChars) ||
    replaceRegexpAll(
            substring(input, showChars + 1),
            '.',
            '*'
    )
);

This function can then be used with varying parameters to achieve different masking patterns.

View-Based Masking

Views offer a powerful approach to implementing column-level security by embedding masking logic directly into the view definition:

-- Create masked view of customer data
CREATE VIEW masked_customers AS
SELECT
    id,
    name,
    maskCustomData(ssn, 0) AS ssn,
    maskCustomData(phone, 3) AS phone,
    city,
    state
FROM customers;

-- Grant access to analysts
GRANT SELECT ON masked_customers
TO analyst_role;

-- Revoke direct table access
REVOKE SELECT ON customers
FROM analyst_role;

Encryption Options in ClickHouse

Encryption protects data from unauthorized access by converting it into an encoded format that can only be deciphered with the appropriate encryption keys.

Disk-Level Encryption

Disk-level encryption protects data at rest by encrypting the entire storage layer. Options include OS-level encryption, filesystem encryption, hardware encryption, and ClickHouse native encryption features.

Column Encryption

For more selective protection, column encryption allows specific sensitive columns to be encrypted while leaving other data in cleartext:

-- Create function for encryption
CREATE FUNCTION encryptAES256 AS (data, key) -> encrypt('aes-256-cbc',
    data,
    key);

-- Create function for decryption
CREATE FUNCTION decryptAES256 AS
(data, key) -> decrypt('aes-256-cbc',
    data,
    key);

-- Secure key retrieval function
CREATE FUNCTION getSecureKey AS
() -> extractFromConfig('encryption_keys.user_data');

Securing Data in Transit

ClickHouse supports encryption for data in transit using SSL/TLS protocols. The server configuration includes settings for certificates, keys, and encryption parameters:

8443
/path/to/server.crt
/path/to/server.key
/path/to/ca.crt
strict
true
true
sslv2,sslv3,tlsv1
true

Advanced Data Masking Implementations

Role-Based Masking with Row Policy Filters

By combining row-level security with role-based access control, administrators can create policies that dynamically filter data based on user roles:

CREATE ROW POLICY sensitive_data
ON customers
FOR SELECT USING (
    hasRole('regular_user') AND (showCustomerData = 0)
    OR hasRole('admin')
);

Materialized Views with Conditional Masking

Materialized views can pre-compute masked data with conditional logic based on user roles:

CREATE MATERIALIZED VIEW customer_data_secure
ENGINE = MergeTree()
ORDER BY id
AS SELECT
    id,
    if(hasRole('admin') OR showCustomerData = 1,
        full_name,
        concat(substring(full_name, 1, 1), '***')
    ) AS name,
    if(hasRole('admin') OR showCustomerData = 1,
        email,
        maskEmail(email)
    ) AS email,
    if(hasRole('admin') OR showCustomerData = 1,
        phone,
        maskPhone(phone)
    ) AS phone
FROM customers;

Regulatory Compliance and Best Practices

GDPR Compliance

Implementation example for GDPR compliance:

CREATE TABLE gdpr_compliant_data
(
    user_id UInt64,
    name String,
    email String,
    preferences String,
    last_activity Date,
    created_at DateTime
)
ENGINE = MergeTree()
ORDER BY user_id
TTL last_activity + INTERVAL 2 YEAR;

CREATE PROCEDURE forget_user(user_id UInt64)
AS BEGIN
    ALTER TABLE user_data DELETE WHERE user_id = user_id;
    ALTER TABLE user_preferences DELETE WHERE user_id = user_id;
    ALTER TABLE user_activity DELETE WHERE user_id = user_id;
END;

Conclusion

Implementing robust security in ClickHouse requires a multi-layered approach combining user management, authentication, data masking, encryption, and monitoring. Key takeaways include adopting defense-in-depth strategies, balancing security with performance, maintaining continuous monitoring, and aligning with regulatory requirements.

Download Data-Security-and-Data-Masking-in-ClickHouse.pdf for detailed implementation examples and advanced configurations.

Mastering User Management in ClickHouse: A Complete Guide to Authentication, Authorization, and Future Security Enhancements

Implementing Custom Access Policies in ClickHouse: A Comprehensive Guide

Efficient Strategies for Purging Data in ClickHouse: Real-Life Use Cases and Detailed Implementation

Most Common ClickHouse Analytical Models