PII & Data Masking in Data Engineering on AWS

1. Introduction

This lesson covers the importance of Personally Identifiable Information (PII) and data masking in the context of data engineering on AWS. As data privacy regulations grow strict, understanding how to handle PII securely becomes paramount.

2. Understanding PII

Personally Identifiable Information (PII) refers to any data that could potentially identify a specific individual. This includes, but is not limited to:

• Name
• Social Security Number
• Email Address
• Phone Number
• Home Address

Handling PII requires compliance with laws such as GDPR, HIPAA, and CCPA.

3. Data Masking Techniques

Data masking is the process of obscuring specific data within a database to protect it. Here are some common techniques:

1. Static Data Masking: Altering data in a database for non-production environments.
2. Dynamic Data Masking: Providing a masked view of the data while keeping the original data intact.
3. Tokenization: Replacing sensitive data with non-sensitive equivalents (tokens).
4. Encryption: Transforming data into a format that is unreadable without a decryption key.

4. AWS Tools for Data Masking

AWS offers various tools and services to help with data masking:

• AWS Glue: A fully managed ETL service that can be used to transform and mask data.
• AWS Lambda: Serverless computing that can be used to execute data masking scripts on demand.
• AWS KMS (Key Management Service): For managing encryption keys securely.

import boto3

# Example of using AWS Glue to transform data
glue = boto3.client('glue')

response = glue.start_job_run(JobName='masking_job')
print(response)

5. Best Practices

To ensure effective data masking and compliance, follow these best practices:

1. Always assess the sensitivity of data before applying masking.
2. Implement access controls to limit who can view unmasked data.
3. Regularly review and update masking strategies as regulations evolve.
4. Test masked data to ensure it meets application requirements.

6. FAQ