Comprehensive Guide to Data Security & Masking in ClickHouse: Access Controls, Encryption, and Regulatory Compliance

This technical playbook provides database administrators and data engineers with actionable strategies for securing sensitive data in ClickHouse. It details:
• Granular Access Controls: Step-by-step implementation of RBAC, row-level security policies, and LDAP/Kerberos authentication
• Advanced Data Masking: Built-in functions (`maskEmail`, `maskPhone`), custom UDFs, and dynamic view-based masking tied to user roles
• Enterprise Encryption: Column-level AES-256 encryption, SSL/TLS configuration, and secure internode communication setups
• Compliance Frameworks: GDPR data minimization workflows, HIPAA-compliant PHI protection, and PCI DSS cardholder data masking
Included are real-world implementations for financial, healthcare, and e-commerce use cases, performance optimization guidelines for security features, and troubleshooting workflows for common issues like access denials and encryption bottlenecks. The guide also outlines future-ready security patterns using materialized views, context-aware masking, and automated audit logging through `system.query_log` analysis.
Practical SQL examples demonstrate secure user provisioning, test data generation with `INSERT SELECT`, and GDPR-compliant TTL configurations. A security implementation checklist and regulatory alignment matrix help organizations balance protection mandates with analytical performance requirements.

Transcript

1. Data Security and Data Masking in ClickHouse Welcome to this comprehensive guide on implementing robust data security and effective data masking techniques in ClickHouse. Throughout this presentation, we'll explore the wide range of security features available in ClickHouse that help organizations protect sensitive information while maintaining database performance and functionality. We'll cover everything from basic access controls to advanced encryption methods, providing practical implementation guidance for database administrators and data engineers. Let's dive into the world of data protection in ClickHouse! by Shiv Iyer
2. Agenda: Data Security & Masking Overview Access Control & Authentication User management, RBAC, and authentication methods Data Masking Techniques SQL functions, custom UDFs, and view-based masking Encryption Options Disk-level, column-level, and in-transit encryption Advanced Implementations Complex solutions using materialized views and policies
3. Why Data Security Matters in ClickHouse Regulatory Compliance Meet GDPR, HIPAA, PCI DSS, and other regulatory requirements through proper data protection measures. Breach Prevention Protect against unauthorized access and potential data leaks that could damage reputation and finances. Data Governance Maintain control over who can access what data, ensuring proper data stewardship throughout your organization.
4. User Management Fundamentals Creating Users with Specific Privileges Define granular access permissions to limit data exposure based on job requirements. Role-Based Access Control (RBAC) Group common permissions into roles to simplify administration and ensure consistency. Row-Level Security Policies Implement data filtering at the row level to show only appropriate data to specific users.
5. Creating Users with Specific Privileges 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; SQL Commands Best Practices Follow the principle of least privilege Regularly audit user privileges Implement password policies Remove unused accounts promptly Document access grants for compliance
6. Role-Based Access Control Implementation Create Roles Define roles that represent job functions or responsibilities within your organization. Assign Privileges Grant specific database permissions to each role based on access requirements. Assign Users to Roles Link users to appropriate roles instead of managing individual permissions. Review & Update Regularly audit role assignments and adjust as organizational needs change.
7. RBAC SQL Examples -- 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; Creating and Assigning Roles -- Create nested roles CREATE ROLE junior_analyst; CREATE ROLE senior_analyst; -- Set up hierarchy GRANT SELECT ON analytics.public_* TO junior_analyst; GRANT junior_analyst TO senior_analyst; GRANT SELECT ON analytics.sensitive_* TO senior_analyst; Using Role Hierarchies
8. Row-Level Security Policies 1 Granular Data Access Filter data at the row level based on user context 2 Unified Data Model Maintain a single table while controlling visibility 3 Transparent Security Seamless to end users and applications
9. Implementing Row-Level Security -- 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'); Example Policy Definition Key Considerations Define policies based on business rules Use user context variables or functions Combine multiple conditions for complex access patterns Test thoroughly to avoid unintended access restrictions Document policies for compliance audits
10. Authentication Methods in ClickHouse 3 Password Authentication Basic method using SHA-256 password hashing for user verification SSL Certificates Certificate-based authentication for stronger security without password transmission LDAP Integration Centralized authentication using enterprise directory services External Authentication Support for Kerberos and custom authentication plugins
11. Setting Up Password Authentication sha256_password * Server Configuration -- 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'; Creating Users with Passwords
12. SSL Certificate Authentication Generate Certificates Create SSL certificates for your ClickHouse server and clients using a trusted Certificate Authority. Configure Server Set up the server to require and validate client certificates for authentication. Distribute Client Certs Securely provide certificates to authorized clients that need to connect to ClickHouse. Configure Clients Set up clients to present their certificates when connecting to the ClickHouse server.
13. LDAP Integration Benefits Centralized user management Simplified authentication Integration with enterprise systems Enforcement of password policies Reduced administrative overhead ldap.example.com 636 uid={user_name},ou=users,dc=example,dc=com 300 true tls1.2 demand Configuration
14. Introduction to Data Masking Definition Data masking is the process of hiding original data with modified content while preserving the data format and usability for non- sensitive purposes. Purpose Protect sensitive information while allowing access to data structure for development, testing, or analysis without exposing protected information. Common Use Cases Customer data protection, PII compliance, test environment security, and limited data sharing with third parties or contractors.
15. Data Masking Approaches in ClickHouse Built-in SQL Functions Use ClickHouse's native functions like maskPhone() and maskEmail() for standard masking operations Custom User-Defined Functions Create specialized masking logic with UDFs for unique requirements View-Based Masking Implement column-level security using views with embedded masking logic 4 Advanced Solutions Combine materialized views and access policies for comprehensive masking systems
16. Using Built-in SQL Functions for Masking Available Functions maskPhone() - Masks phone numbers maskEmail() - Masks email addresses maskCardNumber() - Masks credit card numbers maskData() - General purpose masking SELECT id, name, maskPhone(phone_number) AS masked_phone, maskEmail(email) AS masked_email, maskCardNumber(credit_card) AS masked_cc FROM customers; -- Results: -- 1, John Doe, +1-XXX-XXX-3456, j***@example.com, XXXX-XXXX-XXXX-1234 Implementation Example
17. Masking Function Behavior Function Input Output Description maskPhone() +1-123-456- 7890 +1-XXX-XXX- 7890 Keeps country code and last 4 digits maskEmail() john.doe@e xample.com j***@exampl e.com Keeps first letter and domain maskCardNum ber() 1234-5678- 9012-3456 XXXX-XXXX- XXXX-3456 Keeps only last 4 digits maskData() Secret123 XXXXXXX123 Configurable masking behavior
18. Creating Custom Masking UDFs CREATE FUNCTION maskCustomData AS (input, showChars) -> if( length(input) <= showChars, input, substring(input, 1, showChars) || replaceRegexpAll( substring(input, showChars + 1), '.', '*' ) ); Custom Function Definition -- Show first 2 characters SELECT maskCustomData('12345678', 2) AS result; -- Returns: 12****** -- Show first 4 characters SELECT maskCustomData('ABCDEFGH', 4) AS result; -- Returns: ABCD**** -- Varying amounts of visible data SELECT maskCustomData(full_name, 1) AS name, maskCustomData(ssn, 0) AS ssn, maskCustomData(phone, 6) AS phone FROM customer_data; Function Usage
19. Column-Level Security with Views Create Base Tables Store complete data in base tables with restricted access 2 Define Masked Views Create views that apply masking functions to sensitive columns Grant Access to Views Allow appropriate roles to query masked views instead of base tables
20. Implementing View-Based Masking -- 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; View Definition Benefits Granular column-level protection Simplified access control Consistent application of masking rules Centralized management of masking logic Transparent to end users and applications
21. Encryption Options Overview Disk-Level Encryption Protect data at rest by encrypting the entire storage layer, securing all database files when the system is offline. Column Encryption Selectively encrypt sensitive columns within tables, maintaining granular protection even during database operation. Data in Transit Secure information as it travels between clients and servers or between cluster nodes using encryption protocols.
22. Disk-Level Encryption Options OS-level encryption: Linux dm-crypt, LUKS Filesystem encryption: EncFS, eCryptfs Hardware encryption: Self-encrypting drives ClickHouse native encryption: Built-in encryption features /path/to/encrypted/storage/ /path/to/key.file encrypted Configuration Example
23. Column Encryption 1 Generate and Secure Encryption Keys Create strong encryption keys and implement a secure key management system. 2 Choose Columns to Encrypt Identify sensitive columns that require encryption while considering performance impact. Implement Encryption Functions Use built-in encrypt/decrypt functions or create custom encryption UDFs. 4 Control Access to Decryption Limit decryption capabilities to authorized users with appropriate permissions.
24. Column Encryption Implementation -- 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'); Encryption Logic -- Insert with encryption INSERT INTO sensitive_data SELECT id, name, encryptAES256(social_security_number, getSecureKey()) AS encrypted_ssn FROM source_data; -- Query with decryption SELECT id, name, decryptAES256(encrypted_ssn, getSecureKey()) AS ssn FROM sensitive_data WHERE id = 123; Usage in Table Operations
25. Securing Data in Transit 443 Default SSL Port Standard secure port for ClickHouse HTTPS connections 9440 Secure Native Protocol Default port for encrypted native protocol 256 Bit Encryption Recommended SSL encryption strength
26. SSL/TLS Configuration 8443 /path/to/server.crt /path/to/server.key /path/to/ca.crt strict true true sslv2,sslv3,tlsv1 true Server Configuration clickhouse-client \ --secure \ --host=example.com \ --port=8443 \ --ssl_cert_file=/path/to/client.crt \ --ssl_key_file=/path/to/client.key \ --ssl_ca_file=/path/to/ca.crt jdbc:clickhouse://example.com:8443/default? ssl=true&sslmode=strict&sslrootcert=/path/to/ca.crt Client Configuration
27. Secure Internode Communication Generate Node Certificates Create individual certificates for each node in your ClickHouse cluster. Configure Interserver Encryption Set up encryption parameters for communication between cluster nodes. Verify Certificate Trust Ensure all nodes properly verify certificates from other nodes. Test Secure Communication Validate that nodes can securely communicate using encrypted channels.
28. Internode Encryption Configuration 9010 interserver password Configuration Settings node1.example.com 9010 1 node2.example.com 9010 1 Cluster Definition
29. Advanced Data Masking Implementation Role-Based Masking Dynamic masking based on user role 2 Row Policy Filters Combine with row-level security Materialized Views Pre-computed masked data
30. Creating Row Policies for Different User Roles -- Create policy for different user roles CREATE ROW POLICY sensitive_data ON customers FOR SELECT USING ( -- Regular users see only their data hasRole('regular_user') AND (showCustomerData = 0) OR -- Admins see everything hasRole('admin') ); Policy Definition Usage Considerations Combine multiple conditions for complex access patterns Use context variables to dynamically filter data Create separate policies for different operations (SELECT, INSERT, etc.) Test thoroughly to avoid unintended restrictions Document policies for auditing and compliance
31. Materialized Views with Conditional Masking -- Create materialized view with conditional masking CREATE MATERIALIZED VIEW customer_data_secure ENGINE = MergeTree() ORDER BY id AS SELECT id, -- Conditional name masking if(hasRole('admin') OR showCustomerData = 1, full_name, concat(substring(full_name, 1, 1), '***') ) AS name, -- Conditional email masking if(hasRole('admin') OR showCustomerData = 1, email, maskEmail(email) ) AS email, -- Conditional phone masking if(hasRole('admin') OR showCustomerData = 1, phone, maskPhone(phone) ) AS phone FROM customers; View Definition Benefits Pre-computed masked data for performance Role-based conditional masking Consistent application of masking rules Reduced query complexity for end users Centralized definition of masking logic
32. Dynamic Data Masking with User Contexts 1 User Authentication Establish user identity and role Context Variables Set session-specific masking controls 3 Conditional Masking Apply masking based on context Data Presentation Show appropriately masked results
33. Implementing User Context Variables -- At session start SET allow_sensitive_data = 1; SET current_department_id = 42; SET current_customer_id = 12345; -- In application code (example) connection.execute( "SET allow_sensitive_data = ?", [user.hasPermission("view_sensitive") ? 1 : 0] ); connection.execute( "SET current_department_id = ?", [user.departmentId] ); Setting Context Variables -- In view definition CREATE VIEW employee_data AS SELECT id, name, IF(allow_sensitive_data = 1, salary, NULL) AS salary, department_id, IF(allow_sensitive_data = 1 OR department_id = current_department_id, phone, maskPhone(phone)) AS phone FROM employees WHERE department_id = current_department_id OR allow_sensitive_data = 1; Using Context in Queries
34. Masking in Development and Testing Environments 1 Production Data Original sensitive information in secure environment Data Masking Apply consistent masking before data migration Test Environment Use masked data for development and testing Regular Refresh Update test data with newly masked production data
35. Creating Test Data with INSERT SELECT -- Copy and mask data to test environment INSERT INTO test.customers SELECT id, maskCustomData(name, 1) AS name, maskPhone(phone) AS phone, maskEmail(email) AS email, replaceAll(address, '.', '*') AS address, maskCardNumber(credit_card) AS credit_card, region, registration_date FROM prod.customers; Data Masking During Migration Consistent Masking Approach When creating test data from production, it's essential to: Apply consistent masking rules across all sensitive fields Preserve data relationships and referential integrity Maintain data format and validation rules Document the masking approach for developers Automate the refresh process with scheduled jobs
36. Testing Security Implementations 1 Verify Access Controls Test that users can only access data appropriate for their roles and permissions. Validate Masking Rules Confirm that masking functions properly obscure sensitive data according to specifications. Audit Encryption Verify that encrypted data cannot be accessed without proper decryption keys. Attempt Security Bypass Try to circumvent security measures to identify potential vulnerabilities.
37. Security Testing SQL Examples -- Test as regular user SET user_name = 'regular_user'; -- Attempt to access restricted table SELECT * FROM sensitive_data; -- Should fail or return filtered results -- Test as admin SET user_name = 'admin_user'; -- Attempt same query SELECT * FROM sensitive_data; -- Should return complete results -- Test row-level policy SET current_region_id = 5; SELECT * FROM regional_data; -- Should only show region 5 data Testing Access Controls -- Test masking functions directly SELECT maskPhone('+1-123-456-7890') AS masked_phone, '+1-123-456-7890' AS original_phone; -- Should show masked version -- Test conditional masking SET show_sensitive = 0; SELECT email FROM customer_view WHERE id = 1; -- Should show masked email SET show_sensitive = 1; SELECT email FROM customer_view WHERE id = 1; -- Should show original email if authorized Testing Masking Rules
38. Auditing Security Measures Enable Comprehensive Logging Configure detailed logging of all data access, especially for sensitive information. Regular Security Reviews Schedule periodic audits of security configurations, permissions, and access patterns. Monitor Suspicious Activity Implement alerts for unusual data access patterns or potential security violations. Maintain Compliance Documentation Keep detailed records of security measures for regulatory compliance requirements.
39. Configuring Security Logging trace /var/log/clickhouse-server/clickhouse-server.log /var/log/clickhouse-server/clickhouse-server.err.log 1000M 10 system query_log toYYYYMM(event_date) 7500 Log Configuration -- Find all queries accessing sensitive tables SELECT query_id, user, query_start_time, query FROM system.query_log WHERE query LIKE '%sensitive_data%' AND event_date >= today() - 7 ORDER BY query_start_time DESC; -- Check failed authentication attempts SELECT event_time, user, auth_type, error_code, error_message FROM system.text_log WHERE event_type = 'AuthenticationFailed' AND event_date >= today() - 7; Querying Audit Logs
40. Performance Considerations for Security Features 3 Data Masking Minimal impact when using built-in functions; more complex UDFs may have higher CPU usage Encryption Column encryption adds significant CPU overhead; disk encryption primarily affects I/O operations Row Policies May reduce query performance when filtering large datasets, especially with complex conditions Materialized Views Initial creation requires resources, but subsequent queries benefit from pre- computed results
41. Optimizing Security Performance Indexing Strategy Ensure proper indexes on columns used in security filters to minimize scan operations. Caching Mechanisms Utilize ClickHouse's caching features to reduce the overhead of repeated security checks. Resource Allocation Allocate sufficient CPU and memory resources to handle additional security processing. Data Partitioning Structure data partitions to align with security boundaries for more efficient filtering.
42. Balancing Security and Performance Security Feature Performance Impact Optimization Strategy Row-Level Security Medium to High Use materialized views for common filters Column Masking Low to Medium Optimize UDFs, use built-in functions Column Encryption High Encrypt only essential columns Disk Encryption Low (CPU), Medium (I/O) Use hardware acceleration if available SSL/TLS Low to Medium Session caching, connection pooling
43. Best Practices for Data Security in ClickHouse Defense in Depth Implement multiple security layers rather than relying on a single protection mechanism Principle of Least Privilege Grant minimum necessary access rights to users and applications Monitor and Audit Maintain comprehensive logging and regular security reviews
44. Security Implementation Checklist User Management Create specific users for each purpose Implement role-based access control Enforce strong password policies Regularly audit user accounts Remove unused accounts promptly Data Protection Identify and classify sensitive data Apply appropriate masking techniques Implement encryption for sensitive columns Configure secure data backups Test security measures regularly System Security Enable secure authentication Configure SSL/TLS for all connections Secure internode communications Keep ClickHouse updated Monitor system logs for anomalies
45. Regulatory Compliance and ClickHouse Security GDPR Compliance Implement data minimization, masking, and right-to-be- forgotten capabilities to meet European privacy requirements. HIPAA Requirements Apply PHI protection through encryption, access controls, and comprehensive audit logs for healthcare data. PCI DSS Standards Secure payment card information using strong encryption, masking, and strict access limitations to meet payment industry requirements. SOC 2 Auditing Enable comprehensive logging and security controls to demonstrate proper system security during compliance audits.
46. GDPR-Specific Configuration Data Protection Features Right to be forgotten: Implement deletion procedures Data minimization: Only store necessary fields Purpose limitation: Use row policies to restrict access Storage limitation: Configure TTL for data expiration Processing security: Apply masking and encryption -- Implement TTL for data expiration 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 -- Auto-delete after 2 years of inactivity TTL last_activity + INTERVAL 2 YEAR; -- Data deletion procedure 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; Implementation Example
47. Real-World Security Implementation Scenarios Financial Services Banks implementing column-level encryption for account data and transaction details, with role-based access for different staff positions. Healthcare Medical systems using comprehensive data masking for PHI in test environments while maintaining data utility for development. E-Commerce Online retailers applying dynamic masking for customer profiles based on service representative roles and access needs.
48. Troubleshooting Security Issues Common Problems Access denied errors: Users unable to access needed data due to overly restrictive policies 1. Performance degradation: Queries running slowly after implementing security measures 2. Inconsistent masking: Data appearing masked in some queries but not in others 3. Certificate errors: SSL connection failures due to certificate misconfigurations 4. Key management issues: Problems with encryption key access or rotation 5. Diagnostic Approaches Check system.query_log for errors and execution details Verify user grants with SHOW GRANTS FOR user Inspect role assignments with SHOW CREATE USER Test security functions directly with simple queries Review server logs for authentication errors Use EXPLAIN to analyze query execution with security predicates
49. Future Security Enhancements in ClickHouse The ClickHouse security landscape continues to evolve with upcoming features like enhanced anomaly detection for identifying suspicious access patterns, improved integration with zero-trust security frameworks, and preparation for post- quantum cryptography. Watch for advancements in unified security management that will simplify configuration and monitoring while strengthening protection.
50. Key Takeaways: Data Security & Masking in ClickHouse 1 Defense in Depth Combine multiple security techniques including access control, masking, and encryption 2 Performance Balance Carefully implement security features with performance considerations in mind 3 Continuous Monitoring Maintain comprehensive logging and regular security reviews 4 Regulatory Alignment Configure security measures to meet relevant compliance requirements