LogoLogo
2024.2
  • Immuta Documentation - 2024.2
  • What is Immuta?
  • Self-Managed Deployment
    • Getting Started
    • Deployment Requirements
    • Install
      • Managed Public Cloud
      • Red Hat OpenShift
      • Generic Installation
      • Immuta in an Air-Gapped Environment
      • Deploy Immuta without Elasticsearch
    • Configure
      • Ingress Configuration
      • Cosign Verification
      • TLS Configuration
      • Immuta in Production
      • External Cache Configuration
      • Rotating Credentials
      • Enabling Legacy Query Engine and Fingerprint
    • Upgrade
      • Upgrade Immuta
      • Upgrade to Immuta 2024.2 LTS
    • Disaster Recovery
    • Troubleshooting
    • Conventions
    • Release Notes
  • Data and Integrations
    • Immuta Integrations
    • Snowflake
      • Getting Started
      • How-to Guides
        • Configure a Snowflake Integration
        • Snowflake Table Grants Migration
        • Edit or Remove Your Snowflake Integration
        • Integration Settings
          • Enable Snowflake Table Grants
          • Use Snowflake Data Sharing with Immuta
          • Configure Snowflake Lineage Tag Propagation
          • Enable Snowflake Low Row Access Policy Mode
            • Upgrade Snowflake Low Row Access Policy Mode
      • Reference Guides
        • Snowflake Integration
        • Snowflake Data Sharing
        • Snowflake Lineage Tag Propagation
        • Snowflake Low Row Access Policy Mode
        • Snowflake Table Grants
        • Warehouse Sizing Recommendations
      • Phased Snowflake Onboarding Concept Guide
    • Databricks Unity Catalog
      • Getting Started
      • How-to Guides
        • Configure a Databricks Unity Catalog Integration
        • Migrate to Unity Catalog
      • Databricks Unity Catalog Integration Reference Guide
    • Databricks Spark
      • How-to Guides
        • Configuration
          • Simplified Databricks Configuration
          • Manual Databricks Configuration
          • Manually Update Your Databricks Cluster
          • Install a Trusted Library
        • DBFS Access
        • Limited Enforcement in Databricks
        • Hide the Immuta Database in Databricks
        • Run spark-submit Jobs on Databricks
        • Configure Project UDFs Cache Settings
        • External Metastores
      • Reference Guides
        • Databricks Spark Integration
        • Databricks Spark Pre-Configuration Details
        • Configuration Settings
          • Cluster Policies
            • Python & SQL
            • Python & SQL & R
            • Python & SQL & R with Library Support
            • Scala
            • Sparklyr
          • Environment Variables
          • Ephemeral Overrides
          • Py4j Security Error
          • Scala Cluster Security Details
          • Databricks Security Configuration for Performance
        • Databricks Change Data Feed
        • Databricks Libraries Introduction
        • Delta Lake API
        • Spark Direct File Reads
        • Databricks Metastore Magic
    • Starburst (Trino)
      • Getting Started
      • How-to Guides
        • Configure Starburst (Trino) Integration
        • Customize Read and Write Access Policies for Starburst (Trino)
      • Starburst (Trino) Integration Reference Guide
    • Redshift
      • Getting Started
      • How-to Guides
        • Configure Redshift Integration
        • Configure Redshift Spectrum
      • Reference Guides
        • Redshift Integration
        • Redshift Pre-Configuration Details
    • Azure Synapse Analytics
      • Getting Started
      • Configure Azure Synapse Analytics Integration
      • Reference Guides
        • Azure Synapse Analytics Integration
        • Azure Synapse Analytics Pre-Configuration Details
    • Amazon S3
    • Google BigQuery
    • Legacy Integrations
      • Securing Hive and Impala Without Sentry
      • Enabling ImmutaGroupsMapping
    • Registering Metadata
      • Data Sources in Immuta
      • Register Data Sources
        • Create a Data Source
        • Create an Amazon S3 Data Source
        • Create a Google BigQuery Data Source
        • Bulk Create Snowflake Data Sources
      • Data Source Settings
        • How-to Guides
          • Manage Data Sources and Data Source Settings
          • Manage Data Source Members
          • Manage Access Requests and Tasks
          • Manage Data Dictionary Descriptions
          • Disable Immuta from Sampling Raw Data
        • Data Source Health Checks Reference Guide
      • Schema Monitoring
        • How-to Guides
          • Run Schema Monitoring and Column Detection Jobs
          • Manage Schema Monitoring
        • Reference Guides
          • Schema Monitoring
          • Schema Projects
        • Why Use Schema Monitoring?
    • Catalogs
      • Getting Started with External Catalogs
      • Configure an External Catalog
      • Reference Guides
        • External Catalogs
        • Custom REST Catalogs
          • Custom REST Catalog Interface Endpoints
    • Tags
      • How-to Guides
        • Create and Manage Tags
        • Add Tags to Data Sources and Projects
      • Tags Reference Guide
  • People
    • Getting Started
    • Identity Managers (IAMs)
      • How-to Guides
        • Microsoft Entra ID
        • Okta LDAP Interface
        • Okta and OpenID Connect
        • Integrate Okta SAML SCIM with Immuta
        • OneLogin with OpenID
        • Configure SAML IAM Protocol
      • Reference Guides
        • Identity Managers
        • SAML Single Logout
        • SAML Protocol Configuration Options
    • Immuta Users
      • How-to Guides
        • Managing Personas and Permissions
        • Manage Attributes and Groups
        • User Impersonation
        • External User ID Mapping
        • External User Info Endpoint
      • Reference Guides
        • Attributes and Groups in Immuta
        • Permissions and Personas
  • Discover Your Data
    • Getting Started
    • Introduction
    • Architecture
    • Data Discovery
      • How-to Guides
        • Enable Sensitive Data Discovery (SDD)
        • Manage Identification Frameworks
        • Manage Patterns
        • Manage Rules
        • Manage SDD on Data Sources
        • Manage Global SDD Settings
        • Migrate From Legacy to Native SDD
      • Reference Guides
        • How Competitive Pattern Analysis Works
        • Built-in Pattern Reference
        • Built-in Discovered Tags Reference
    • Data Classification
      • How-to Guides
        • Activate Classification Frameworks
        • Adjust Identification and Classification Framework Tags
        • How to Use a Built-In Classification Framework with Your Own Tags
      • Built-in Classification Frameworks Reference Guide
  • Detect Your Activity
    • Getting Started
      • Monitor and Secure Sensitive Data Platform Query Activity
        • User Identity Best Practices
        • Integration Architecture
        • Snowflake Roles Best Practices
        • Register Data Sources
        • Automate Entity and Sensitivity Discovery
        • Detect with Discover: Onboarding Guide
        • Using Immuta Detect
      • General Immuta Configuration
        • User Identity Best Practices
        • Integration Architecture
        • Databricks Roles Best Practices
        • Register Data Sources
    • Introduction
    • Audit
      • How-to Guides
        • Export Audit Logs to S3
        • Export Audit Logs to ADLS
        • Run Governance Reports
      • Reference Guides
        • Universal Audit Model (UAM)
        • Snowflake Query Audit Logs
        • Databricks Unity Catalog Audit Logs
        • Databricks Query Audit Logs
        • Starburst (Trino) Query Audit Logs
        • UAM Schema
        • Audit Export CLI
        • Governance Report Types
      • Deprecated Audit Guides
        • Legacy to UAM Migration
        • Download Audit Logs
        • System Audit Logs
    • Detection
      • Use the Detect Dashboards
      • Reference Guides
        • Detect
        • Detect Dashboards
        • Unknown Users in Audit Logs
    • Monitors
      • Manage Monitors and Observations
      • Detect Monitors Reference Guide
  • Secure Your Data
    • Getting Started with Secure
      • Automate Data Access Control Decisions
        • The Two Paths: Orchestrated RBAC and ABAC
        • Managing User Metadata
        • Managing Data Metadata
        • Author Policy
        • Test and Deploy Policy
      • Compliantly Open More Sensitive Data for ML and Analytics
        • Managing User Metadata
        • Managing Data Metadata
        • Author Policy
      • Federated Governance for Data Mesh and Self-Serve Data Access
        • Defining Domains
        • Managing Data Products
        • Managing Data Metadata
        • Apply Federated Governance
        • Discover and Subscribe to Data Products
    • Introduction
      • Scalability and Evolvability
      • Understandability
      • Distributed Stewardship
      • Consistency
      • Availability of Data
    • Authoring Policies in Secure
      • Authoring Policies at Scale
      • Data Engineering with Limited Policy Downtime
      • Subscription Policies
        • How-to Guides
          • Author a Subscription Policy
          • Author an ABAC Subscription Policy
          • Subscription Policies Advanced DSL Guide
          • Author a Restricted Subscription Policy
          • Clone, Activate, or Stage a Global Policy
        • Reference Guides
          • Subscription Policies
          • Subscription Policy Access Types
          • Advanced Use of Special Functions
      • Data Policies
        • Overview
        • How-to Guides
          • Author a Masking Data Policy
          • Author a Minimization Policy
          • Author a Purpose-Based Restriction Policy
          • Author a Restricted Data Policy
          • Author a Row-Level Policy
          • Author a Time-Based Restriction Policy
          • Certifications Exemptions and Diffs
          • External Masking Interface
        • Reference Guides
          • Data Policy Types
          • Masking Policies
          • Row-Level Policies
          • Custom WHERE Clause Functions
          • Data Policy Conflicts and Fallback
          • Custom Data Policy Certifications
          • Orchestrated Masking Policies
    • Domains
      • Getting Started with Domains
      • Domains Reference Guide
    • Projects and Purpose-Based Access Control
      • Projects and Purpose Controls
        • Getting Started
        • How-to Guides
          • Create a Project
          • Create and Manage Purposes
          • Adjust a Policy
          • Project Management
            • Manage Projects and Project Settings
            • Manage Project Data Sources
            • Manage Project Members
        • Reference Guides
          • Projects and Purposes
          • Policy Adjustments
        • Why Use Purposes?
      • Equalized Access
        • Manage Project Equalization
        • Project Equalization Reference Guide
        • Why Use Project Equalization?
      • Masked Joins
        • Enable Masked Joins
        • Why Use Masked Joins?
      • Writing to Projects
        • How-to Guides
          • Create and Manage Snowflake Project Workspaces
          • Create and Manage Databricks Project Workspaces
          • Write Data to the Workspace
        • Reference Guides
          • Project Workspaces
          • Project UDFs (Databricks)
    • Data Consumers
      • Subscribe to a Data Source
      • Query Data
        • Querying Snowflake Data
        • Querying Databricks Data
        • Querying Databricks SQL Data
        • Querying Starburst (Trino) Data
        • Querying Redshift Data
        • Querying Azure Synapse Analytics Data
      • Subscribe to Projects
  • Application Settings
    • How-to Guides
      • App Settings
      • BI Tools
        • BI Tool Configuration Recommendations
        • Power BI Configuration Example
        • Tableau Configuration Example
      • Add a License Key
      • Add ODBC Drivers
      • Manage Encryption Keys
      • System Status Bundle
    • Reference Guides
      • Data Processing, Encryption, and Masking Practices
      • Metadata Ingestion
  • Releases
    • Immuta v2024.2 Release Notes
    • Immuta Release Lifecycle
    • Immuta LTS Changelog
    • Immuta Support Matrix Overview
    • Immuta CLI Release Notes
    • Immuta Image Digests
    • Preview Features
      • Features in Preview
    • Deprecations
  • Developer Guides
    • The Immuta CLI
      • Install and Configure the Immuta CLI
      • Manage Your Immuta Tenant
      • Manage Data Sources
      • Manage Sensitive Data Discovery
        • Manage Sensitive Data Discovery Rules
        • Manage Identification Frameworks
        • Run Sensitive Data Discovery on Data Sources
      • Manage Policies
      • Manage Projects
      • Manage Purposes
    • The Immuta API
      • Integrations API
        • Getting Started
        • How-to Guides
          • Configure an Amazon S3 Integration
          • Configure an Azure Synapse Analytics Integration
          • Configure a Databricks Unity Catalog Integration
          • Configure a Google BigQuery Integration
          • Configure a Redshift Integration
          • Configure a Snowflake Integration
          • Configure a Starburst (Trino) Integration
        • Reference Guides
          • Integrations API Endpoints
          • Integration Configuration Payload
          • Response Schema
          • HTTP Status Codes and Error Messages
      • Immuta V2 API
        • Data Source Payload Attribute Details
        • Data Source Request Payload Examples
        • Create Policies API Examples
        • Create Projects API Examples
        • Create Purposes API Examples
      • Immuta V1 API
        • Authenticate with the API
        • Configure Your Instance of Immuta
          • Get Fingerprint Status
          • Get Job Status
          • Manage Frameworks
          • Manage IAMs
          • Manage Licenses
          • Manage Notifications
          • Manage Sensitive Data Discovery (SDD)
          • Manage Tags
          • Manage Webhooks
          • Search Filters
        • Connect Your Data
          • Create and Manage an Amazon S3 Data Source
          • Create an Azure Synapse Analytics Data Source
          • Create an Azure Blob Storage Data Source
          • Create a Databricks Data Source
          • Create a Presto Data Source
          • Create a Redshift Data Source
          • Create a Snowflake Data Source
          • Create a Starburst (Trino) Data Source
          • Manage the Data Dictionary
        • Manage Data Access
          • Manage Access Requests
          • Manage Data and Subscription Policies
          • Manage Domains
          • Manage Write Policies
            • Write Policies Payloads and Response Schema Reference Guide
          • Policy Handler Objects
          • Search Audit Logs
          • Search Connection Strings
          • Search for Organizations
          • Search Schemas
        • Subscribe to and Manage Data Sources
        • Manage Projects and Purposes
          • Manage Projects
          • Manage Purposes
        • Generate Governance Reports
Powered by GitBook

Other versions

  • SaaS
  • 2024.3

Copyright © 2014-2024 Immuta Inc. All rights reserved.

On this page
  • Example of anonymizing a column rather than blocking it
  • Using k-anonymization to mask columns
  • Cell-level security

Was this helpful?

Export as PDF
  1. Secure Your Data
  2. Introduction

Availability of Data

PreviousConsistencyNextAuthoring Policies in Secure

Last updated 11 months ago

Was this helpful?

Example of anonymizing a column rather than blocking it

By having highly granular controls coupled with anonymization techniques, more data than ever can be at the fingertips of your analysts and data scientists (in some cases, up to 50% more).

Why is that?

Let’s start with a simple example and get more complex. Obviously, if you can’t do row- and column-level controls and are limited to only GRANTing access to tables, you are either over-sharing or under-sharing. In most cases, it’s under-sharing: there are rows and columns in that table the users can see, just not all of them, but they are blocked completely from the table.

That example was obvious, but it can get a little more complex. If you have column-level controls, now you can give them access to the table, but you can completely hide a column from a user by making all the values in it null, for example. Thus, they’ve lost all data/utility from that column, but at least they can get to the other columns.

That masked column can be more useful, though. If you hash the values in that column instead, utility is gained because the hash is consistent - you can track and group by the values, but can’t know exactly what they are.

But you can make that masked column even more useful! If you use something like instead of hashing, they can know many of the values, but not all of them, gaining almost complete utility from that column. As your anonymization techniques become more advanced, you gain utility from the data while preserving privacy. These are termed privacy enhancing technologies (PETs) and Immuta places them at your fingertips.

This is why advanced anonymization techniques can get significantly more data into your analysts' hands.

Using k-anonymization to mask columns

While columns like first_name, last_name, email, and social security number can certainly be directly identifying, something like gender and race, on the surface, seem like they may not be directly identifying, but it could be. Imagine if there are very few Tongan men in a data set...in fact, for the sake of this example, lets say there’s only one. So if I know of a Tongan man in that company, I can easily run a query like this and figure out that person’s salary without using their name, email, or social security number:

select salary from [table] where race = 'Tongan' and gender = 'Male';

This is the challenge with indirect identifiers. It comes down to how much your adversary, the person trying to break privacy, knows externally, which is unknowable to you. In this case, all they had to know was the person was Tongan and a man (and there happens to be only one of them in the data) to figure out their salary, sensitive information. Let's also pretend the result of that query was a salary of 106072. This is called a linkage attack and is specifically called out in privacy regulations as something you must contend with, for example, from GDPR:

Article 4(1): "Personal data" means any information relating to an identified or identifiable natural person ("data subject"); an identifiable person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that person.

Almost any useful column with many unique values will be a candidate for indirectly identifying an individual, but also be an important column for your analysis. So if you completely hide every possible indirectly identifying column, your data is left useless.

You can solve this problem with PETs. Take note of two things by querying the data:

  • If you only search for “Tongan” alone (no Male), there are several Tongan women, so this linkage attack no longer works: select salary, gender from [table] where race = 'Tongan';

  • There are no null values in the gender or race columns.

Now let's say you apply the k-anonymization masking policy using Immuta.

Then you run this query again to find the Tongan man's salary: select salary from immuta_fake_hr_data where race = 'Tongan' and gender = 'Male';

You get no results.

Now you run this query ignoring the gender: select salary, gender from immuta_fake_hr_data where race = 'Tongan';

Only the women are returned.

The linkage attack was successfully averted. Remember, from our queries prior to the policy, the salary was 106072, so let’s run a query with that: select race, gender from immuta_fake_hr_data where salary = 106072;

There he is! But race will be suppressed (NULL) so this linkage attack will not work. It was also smart enough to not suppress gender because that did not contribute to the attack; suppressing race alone averts the attack. This is the magic of k-anonymization: it provides as much utility as possible while preserving privacy by suppressing values that appear so infrequently (along with other values in that row) that they could lead to a linkage attack.

Cell-level security

Cell-level security is not exactly an advanced privacy enhancing technology (PET) as in the example above, but it does provide impressive granular controls within a column for common use cases.

What is cell-level security?

If you have values in a column that should sometimes be masked, but not always, that is masking at the cell-level, meaning the intersection of a row with a column. What drives whether that cell should be masked or not is some other value (or set of values) in the rest of the row shared with that column (or a joined row from another table).

For example, a user wants to mask the credit card numbers but only when the transaction amount is greater than $500. This allows you to drive masking in a highly granular manner based on other data in your tables.

This technique is also possible using Immuta, and you can leverage tags on columns to drive which column in the row should be looked at to mask the cell in question, providing further scalability.

k-anonymization