Anonymization & Masking

Preserve Privacy While Maintaining Data’s Analytical Value

Anonymize and mask sensitive data to enhance privacy and ensure compliance while maintaining data’s analytical value and usefulness for reporting, data science and machine learning.

Democratize access by
De-Identifying Data

Faced with rigorous privacy and compliance mandates, enterprises need to de-identify sensitive information before storing and analyzing it but without impacting the data’s usefulness.

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Privacera enables various forms of anonymization and masking to ensure data protection and preserve privacy while maintaining the referential integrity and analytical value of the data.

How Anonymization and Masking Works

Preserve Privacy and Insights

Use NIST-based standards for cryptographic encryption to anonymize sensitive data while maintaining its analytical value.

Anonymization with Minimal Performance Impact

Use integration with ETL and data consumption tools to make de-identified data available for analytics with minimal impact on query performance.

Centralized Key Management and Re-Identify Data

Privacera key management is integrated with popular cloud key vaults and enables re-identifying data on-demand.

Frequently Asked Questions

What is the difference between field encryption and masking?

Encryption uses keys and algorithms to create random pseudo characters for a given value. Encrypted values can be reversed with the key and by applying a decryption algorithm. Encryption can be used when data needs to be protected at rest and in use, while enabling certain users to reverse the encryption and get the original data back. Masking data is typically one-way and not reversible. Masking can be used to remove PII data altogether.

Can the encryption keys be stored in an external key store?

Privacera can store keys in external hardware security module (HSM) or cloud-based key vaults.

Resources & Latest News

Whitepaper

Security and Privacy for Modern Data Platforms

Learn how to enable comprehensive security, privacy and governance in big data and cloud environments using Privacera.

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Privacera for Amazon EMR

Use this link to request a Docker package to install fine-grained access control to Amazon EMR.

Datasheet

Data De-Identification

Privacera enables various forms of anonymization and data masking to ensure data protection and privacy preservation while maintaining the referential integrity and analytical value of your sensitive data.

See Anonymization in Action