Privacy Enhancing Technologies

Immuta’s privacy enhancing technologies (PETs) dynamically mask and anonymize sensitive data – with mathematical guarantees – to accelerate data sharing use cases. Data teams can leverage Immuta’s 20+ controls to address any privacy requirement for using and sharing sensitive data, including PII, PHI, or other private information.

Our Value

Unlocking Sensitive Data with PETs

Immuta’s privacy enhancing technologies help organizations across every industry simplify operations, improve data security, and unlock data’s value. With PETs, you can:

Support more permitted use cases so your data can go further

Securely share data both internally and externally for better collaboration

Easily comply with data rules, regulations, and sharing agreements

Increase the ROI on your cloud platforms by unlocking more data for more users


Apply obfuscation techniques, including hashing, rounding, and regular expression, at query runtime – without writing code or copying data.

Anonymization & Pseudonymization

Implement anonymization and pseudonymization techniques when queries are run using plain policies written in plain language, without additional code or copies.

Masking with k-Anonymization

Seamlessly prepare sensitive data for use and prevent re-identification, while eliminating manual approaches that require mathematicians to enforce.

Randomized Response

Achieve local differential privacy to protect specific columns, and enable mathematically guaranteed limits on an attacker’s ability to exploit data.


Enforce controls at query runtime to return a percentage of the data or otherwise restrict access using pre-built privacy controls, and avoid spending time writing new code or copying data.

Covering the Full Data Security Spectrum

Privacy enhancing technologies help ensure your data is protected, but you still need to know where to apply them and verify their efficacy. Immuta provides full data security coverage by allowing you to discover, secure, and monitor data usage to detect risks. See how you can do all three – without sacrificing speed or utility.

Find out more about the Immuta Data Security Platform.


Unlock Your Data

"We’re looking at anonymization of data through the privacy enhancing techniques that Immuta provides, including different accelerators and adding a query layer or data dictionary. These are the primary areas of focus on our roadmap."

Vineeth Menon
Vineeth Menon Head of Data Lake Engineering

clients covered by data privacy in multi-tenant platforms


faster data access while achieving FDA and HIPAA compliance


growth in users accessing data


auditable compliance with GDPR and Schrems II

Frequently Asked Questions

What are the most common de-identification techniques?

There are a range of effective common data de-identification techniques used by modern organizations. These de-identification techniques include k-anonymization, generalization, randomization, pseudonymization combined with data masking, and more. Data can also be de-identified completely through redaction or suppression, but these techniques render the data completely unusable for analysis. Masking techniques de-identify data for security purposes while still leaving it usable for those who need it, balancing security with efficiency.

What is Data De-identification and Why is It Important?
What is k-anonymity?

What is k-anonymity? k-Anonymity is a privacy enhancing technology (PET) that works by combining large sets of data with similar attributes. In doing so, direct or indirect identifiers about any individual contributing to that data set are obscured, often through generalization or suppression of attributes. k-Anonymization is often referred to as the power of “hiding in the crowd,” and is useful in protecting against re-identification.

Everything You Need to Know About K-Anonymity
What is the difference between data masking vs. hashing?

The difference between data masking vs. hashing is that data masking involves removing or hiding sensitive data using a different value that is similar in structure to the original data, while hashing uses a formula to generate a string of characters that is fixed in length. Since hashes are concise and unique, they make large data sets easier to compare, yet make reversal significantly more difficult, if not impossible.

What Is Data Masking?
What is tokenized data?

What is tokenized data? Tokenized data refers to a piece of non-sensitive data with no intrinsic value, known as a token, that stands in for another piece of sensitive information. The concept is akin to using poker chips to represent money. Tokens are stored within a database, so only those with access to the database are able to derive the original information from the tokenized data.

What is Data Tokenization and Why is it Important?
What is an example of PII obfuscation?

Obfuscation of personally identifiable information (PII) limits exposure of the sensitive data in ways that still allows portions of it to be useful. An example of PII obfuscation is nulling a bank account number for anyone except those with authorization to see the full number. This is particularly necessary in instances of fraud detection and analysis, because it allows those involved in fraud investigations to reference exact account numbers, but prevents people in other, unrelated departments from seeing customers’ full account numbers.

What is Data Obfuscation? Everything You Should Know

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