- 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
Control access to S3 buckets with AWS Lambda function on PrivaceraCloud
You can control access to your S3 buckets with a Lambda function to protect them from unauthorized use.
To secure a bucket, you can create a Python function that creates an S3 client to check for proper authorization to access that bucket. If authorization is successful, the desired document is passed to the Privacera dataserver to give the requesting user the access.
A sample Lambda function is provided here.
Prerequisites
You need to make sure of the following:
Your Privacera connection to S3 must have already been created.
Decide which S3 buckets you want to protect.
An S3 policy has been set in Privacera. See Modify Resource Policy.
You need your Privacera access key, secret key, and Privacera datatserver URL. See Get your access key, secret key, and value of PRIVACERA_DS_ENDPOINT_URL.
Get your access key, secret key, and value of PRIVACERA_DS_ENDPOINT_URL
The values for access key, secret key, and the dataserver URL are included in the privacera_aws.sh
script, which is downloadable as detailed in Generate security token.
In that script, use the value of the DS_URL_HOST
variable as the value of the PRIVACERA_DS_ENDPOINT_URL
variable in the Python Lambda function listed in Example Python Lambda for PrivaceraCloud.
Create Python Lambda function in AWS
The Lambda function needs to create an S3 client object with the Privacera dataserver URL as an endpoint URL for S3 with privacera access key and secret key generated for respective user.
The following example program shows the a sample lambda_handler()
function to control access to an array of S3 buckets. You can modify this example or create your own based on it.
Follow Amazon's steps to create a Lambda function. See Getting started with Lambda.
Call the function priv_list_bucket.
In the Create function dashboard, select Author from scratch, and use the following values in creating the function's Basic information:
- Function name: priv_list_bucket
- Runtime: python 3.7
- Architecture: x86_64
In Permissions, for Execution role, select Use an existing role for the Lambda function. This role must have permissions to execute Lambda functions. Example: AWS_Default_Role.
In the displayed priv_list_bucket function dashboard, in the Code source code field, add your Lambda function in lambda_function.py . You can use the example program or your own implementation of it.
In the Runtime settings, the Function name should be
<python_filename>.<function_name>
.In our example, we use
lambda_function.lambda_handler
.To create a new test with an empty JSON input, click Test and Save.
If you see the message Changes not deployed for the test created in the previous step, click Deploy.
Click Test again.
The result of the test is displayed.
Example Python Lambda for PrivaceraCloud
import boto3 import os import requests # Set these variables with the values you # obtained in the prerequisites. PRIVACERA_DS_ENDPOINT_URL = '' PRIVACERA_ACCESS_KEY ='' PRIVACERA_SECRET_ACCESS_KEY = '' def lambda_handler(event, context): session = boto3.session.Session() s3_client = session.client( service_name='s3', aws_access_key_id=PRIVACERA_ACCESS_KEY, aws_secret_access_key=PRIVACERA_SECRET_ACCESS_KEY, endpoint_url=PRIVACERA_DS_ENDPOINT_URL ) allBuckets = s3_client.list_buckets() data = [bucket["Name"] for bucket in allBuckets['Buckets']] return data