- PrivaceraCloud Release 7.4
- Enhancements and updates in PrivaceraCloud release 7.4
- Known Issues in PrivaceraCloud 7.4
- PrivaceraCloud User Guide
- Overview of PrivaceraCloud
- Connect applications with the setup wizard
- Connect applications
- About applications
- Connect Azure Data Lake Storage Gen 2 (ADLS) to PrivaceraCloud
- Connect Amazon Textract to PrivaceraCloud
- Athena
- Privacera Discovery with Cassandra
- Connect Databricks to PrivaceraCloud
- Databricks SQL
- Databricks SQL Overview and Configuration
- Planning and general process
- Prerequisites
- Databricks SQL with Privacera Hive
- Connect Databricks SQL application
- Grant Databricks SQL permissions to PrivaceraCloud users
- Define a resource policy
- Test the policy
- Databricks SQL PolicySync fields
- Configuring column-level access control
- View-based masking functions and row-level filtering
- Create an endpoint in Databricks SQL
- Databricks SQL Fields
- Databricks SQL Hive Service Definition
- Databricks SQL Masking Functions
- Databricks SQL Encryption
- Use a custom policy repository with Databricks
- Connect Databricks SQL to Hive policy repository on PrivaceraCloud
- Databricks SQL Overview and Configuration
- Connect Databricks Unity Catalog to PrivaceraCloud
- Connect S3 to PrivaceraCloud
- Prerequisites in AWS console
- Connect S3 application to PrivaceraCloud
- Enable Privacera Access Management for S3
- Enable Data Discovery for S3
- S3 AWS Commands - Ranger Permission Mapping
- S3
- AWS Access with IAM
- Access AWS S3 buckets from multiple AWS accounts
- Add UserInfo in S3 Requests sent via Dataserver
- Control access to S3 buckets with AWS Lambda function on PrivaceraCloud
- Dremio Plugin
- DynamoDB
- Connect Elastic MapReduce from Amazon application to PrivaceraCloud
- Connect EMR application
- EMR Spark access control types
- PrivaceraCloud configuration
- AWS IAM roles using CloudFormation setup
- Create a security configuration
- Create EMR cluster
- How to configure multiple JSON Web Tokens (JWTs) for EMR
- EMR Native Ranger Integration with PrivaceraCloud
- Connect EMRFS S3 to PrivaceraCloud
- Files
- GBQ
- Google Cloud Storage
- Connect Glue to PrivaceraCloud
- Google BigQuery for PolicySync
- Connect Kinesis to PrivaceraCloud
- Connect Lambda to PrivaceraCloud
- Microsoft SQL Server
- MySQL for Discovery
- Open Source Apache Spark
- Oracle for Discovery
- PostgreSQL
- Connect Power BI to PrivaceraCloud
- Presto
- Redshift
- Snowflake
- Starburst Enterprise with PrivaceraCloud
- Starburst Enterprise Presto
- Trino
- Connect users
- Data access Users, Groups, and Roles
- UserSync
- Portal user LDAP/AD
- Datasource
- Okta Setup for SAML-SSO
- Azure AD setup
- SCIM Server User-Provisioning
- User Management
- Identity
- Access Manager
- Access Manager
- Resource Policies
- Tag Policies
- Scheme Policies
- Service Explorer
- Reports
- Audit
- About data access users, groups, and roles resource policies
- Security zones
- Discovery
- Classifications via random sampling
- Privacera Discovery scan targets
- Propagate Privacera Discovery Tags to Ranger
- Enable offline scanning on Azure Data Lake Storage Gen 2 (ADLS)
- Enable Real-time Scanning of S3 Buckets
- Enable Real-time Scanning on Azure Data Lake Storage Gen 2 (ADLS)
- Enable Discovery Realtime Scanning Using IAM Role
- Encryption
- Overview of Privacera Encryption
- Encryption schemes
- Presentation schemes
- Masking schemes
- Create scheme policies
- Privacera-supplied encryption schemes for the Privacera API
- Privacera-supplied encryption schemes for the Bouncy Castle API
- API date input formats
- Deprecated encryption formats, algorithms, and scopes
- Privacera Encryption REST API
- PEG API endpoint
- PEG REST API encryption endpoints
- Prerequisites
- Common PEG REST API fields
- Construct the datalist for the /protect endpoint
- Deconstruct the response from the /unprotect endpoint
- Example data transformation with the /unprotect endpoint and presentation scheme
- Example PEG API endpoints
- Make encryption API calls on behalf of another user
- Privacera Encryption UDF for masking in Databricks on PrivaceraCloud
- Privacera Encryption UDFs for Trino on PrivaceraCloud
- Syntax of Privacera Encryption UDFs for Trino
- Prerequisites for installing Privacera Crypto plug-in for Trino
- Download and install Privacera Crypto jar
- Set variables in Trino etc/crypto.properties
- Restart Trino to register the Privacera encryption and masking UDFs for Trino
- Example queries to verify Privacera-supplied UDFs
- Privacera Encryption UDF for masking in Trino on PrivaceraCloud
- Encryption UDFs for Apache Spark on PrivaceraCloud
- Launch Pad
- Settings
- Dashboard
- Usage statistics
- Operational status of PrivaceraCloud and RSS feed
- How to Get Support
- Coordinated Vulnerability Disclosure (CVD) Program of Privacera
- Shared Security Model
- PrivaceraCloud Previews
- Preview: File Explorer for S3
- Preview: File Explorer for Azure
- Preview: File Explorer for GCS
- Preview: Scan Generic Records with NER Model
- Preview: Scan Electronic Health Records with NER Model
- Preview: OneLogin setup for SAML-SSO
- Preview: Azure Active Directory SCIM Server UserSync
- Preview: OneLogin UserSync
- Preview: PingFederate UserSync
- Quickstart for Databricks Unity Catalog on PrivaceraCloud
- What do I need to do in my Databricks Workspace?
- Where is the sample dataset in my Databricks Workspace?
- What should I do in the PrivaceraCloud web portal?
- Access use-case - How do I give a user access to a table or restrict from running a SQL select query?
- Access use-case - How do I restrict a user from seeing contents of a column in the result of a SQL select query?
- Column masking use-case - How do I restrict a user from seeing contents of a column by masking the values in the result of a SQL select query?
- Access use-case - How do I disallow a user from seeing certain rows of a table?
- PrivaceraCloud documentation changelog
Configure Policy with Attribute-Based Access Control (ABAC) on PrivaceraCloud
PrivaceraCloud enables use of user, group, resource, classification, and environment attributes in authorization policies. Attribute-Based Access Control (ABAC) makes it possible to express authorization policies without prior knowledge of specific resources or specific users, which helps avoid the need for new policies as new resources or users are introduced.
Overview
The following scenarios can be accomplished with ABAC configuration:
ABAC in row filter expressions
By using Attribute-Based Access Control (ABAC) for row filters, user can define attribute based conditions in row filter expressions. For example, user/group attributes can be referenced in a row filter expression:
dept = ${{USER.dept}} state in ( ${{GET_UG_ATTR_Q_CSV('state')}} )
ABAC in resource definitions
Attributes can also be included in resource names:
path: /home/${{USER._name}} path: /departments/${{USER.dept}} database: dept_${{USER.dept}}p
ABAC in policy conditions in Resource based access policies
With the ABAC feature, you can configure resource policies based on user attributes from your LDAP or AD service.
You can assign attributes to users, groups and tags in policies. You can also implement logical conditions on the user attributes for the resource policies.
Attributes can be referenced using expressions:
USER.employeeType != 'intern' TAG.piiType == 'email' TAG.sensitivityLevel <= USER.allowedSensitivityLevel
Note
ABAC for policy conditions in resource-based access policies is supported for the following data sources:
Databricks/EMR Hive, Spark, and all services using
privacera_hive
service definitions.PolicySync Snowflake
Prerequisites
Import the users from the LDAP or AD directory to the Privacera Ranger database. See Connect users to learn more.
Determine the resources you want to protect with ABAC-based policies.
Setup User/Group Attributes
The users and groups with their attributes can be synced with Usersync from LDAP or AD. With users, the user attributes are also synced from source (such as LDAP or AD).
Add/Edit Attributes in the PrivaceraCloud Portal
Example policy with ABAC
Let's say we have a user with the following attribute:
dept
:IT
We create a resource access policy such as:
Policy Details
Policy Name
access
Normal Enabled
Hive Database
hive_test
Include
Hive Table
employee
Include
Hive Column
*
Include
Description
Audit Log
Yes
Policy Labels
--
Allow Conditions
Select Role
Select Group
Select User
Policy Conditions
Permissions
Delegate Admin
tejas, sally
select
(unchecked)
We created a Row Filter Policy as below:
Policy Name
employee_rlf
Normal Enabled
Hive Database
hive_test
Hive Table
employee
Description
Audit Log
Yes
Policy Labels
--
Row Level Conditions
Select Role
Select Group
Select User
Policy Conditions
Access Types
Row Level Filter
public
select
dept == '${{USER.dept}}'
Note that the row filter expression used here is:
dept == '${{USER.dept}}'
Now the setup is done on EMR Hive. The table
hive_test.employee
is available on the EMR Hive cluster, with data as below:| employee.id | employee.name | employee.location | employee.dept | +--------------+-----------------+--------------------+-----------------+ | 1 | manny | mumbai | IT | 2 | novak | NY | HR | 3 | Pablo | Barcelona | HR | 4 | Carlos | Mexico | Support +--------------+-----------------+--------------------+-----------------+ 4 rows selected
The user
tejas
is available on the EMR cluster to run the query. When select query is executed bytejas
:| employee.id | employee.name | employee.location | employee.dept | +--------------+-----------------+--------------------+-----------------+ | 1 | manny | mumbai | IT +--------------+-----------------+--------------------+-----------------+ 1 row selected