ADF Studio Designing Data Pipeline 2: Data Masking

Data masking and data anonymization are important techniques used to protect sensitive information in the data. Sensitive information can include personal information, financial information, or confidential business information. Data masking and anonymization can be used to redact sensitive information, or to replace it with synthetic data, thus making it possible to use the data for testing, development, and analytics purposes without exposing sensitive information.

Masking and anonymization in ADF

Azure Data Factory provides a robust set of tools for data masking and anonymization. One key benefit of using ADF for data masking and anonymization is that it provides a centralized platform for managing sensitive information. Organizations can use Azure Data Factory to manage their sensitive data, mask and anonymize it, and ensure it is protected from unauthorized access or theft.

Data masking

Data masking, a security technique, conceals sensitive data to prevent unauthorized access. It’s commonly used when sharing sensitive data for development, testing, or analytics, ensuring only authorized users can access it. This involves replacing sensitive values with fictitious ones, allowing data to serve its purpose without revealing the original values. For instance, a social security number (SSN) might display only the last four digits, enabling analysis while concealing the complete SSN. Applicable to various sensitive data types like personally identifiable information (PII), financial, and healthcare data, data masking uses techniques such as substitution, shuffling, encryption, or tokenization, based on security needs and data use.

Data anonymization

Data anonymization safeguards personal information ...