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Data Anonymization for Responsible Use of Sensitive Data

Today’s customers value their privacy, and thanks to legislation such as GDPR and CPRA, organizations are prioritizing data privacy. Data anonymization enables organizations to use sensitive information responsibly. By modifying or removing personally identifiable information (PII) from data sets, sensitive data can be safely analyzed and shared. In this article, we’ll explain how data anonymization works and what types of data should be anonymized. We’ll also explore five common data anonymization methods and share how each one works to protect individual privacy and support compliance with data privacy laws.

What Is Data Anonymization?

Data anonymization is the process of altering or removing personally identifiable information from data sets to protect the privacy of individuals. Its purpose is to transform data so it can’t be linked back to specific individuals, thus preserving anonymity while still maintaining the usefulness of the data for analysis, research and other purposes. Anonymization can be accomplished by replacing the original data with artificial data, rearranging data set attributes in ways that differ from their original form and using machine-generated synthetic data in place of the real thing. 

Definitive Guide to Governance in Snowflake

While data anonymization techniques can play an important role in reducing opportunities for sensitive data to be improperly disclosed, it's not an all-in-one data privacy solution. Data anonymization should be used in conjunction with other data privacy controls, including data access controls such as role-based access control (RBAC) or attribute-based access control (ABAC). Data encryption is another standard method that should be implemented to secure sensitive data. This method uses an encryption key, a mathematically derived key that prevents third parties from reading data while at rest,  in transit or in active use.

What types of data should be anonymized? 

PII is the most common type of data to anonymize. Examples include contact information, date of birth, credit card account numbers and SSNs. PII also covers biometric information such as photographs with identifiable characteristics or voice signatures along with education, employment, financial and medical information. Data anonymization can also be applied to other types of data that must remain confidential, including an organization’s financial reports and intellectual property such as research findings or proprietary manufacturing processes.

5 Common Data Anonymization Approaches

Data anonymization can be accomplished in many ways. Selecting the right data anonymization approach involves a number of factors, including the organization’s data use cases and goals, the data types being used and their sensitivity level.

Data masking

Data masking is one of the most frequently encountered types of data anonymization. This process obscures or alters the values in the original data set by replacing them with artificial data that appears genuine but has no real connection to the original. Data masking allows organizations to retain access to the original data set while being highly resistant to detection or reverse engineering. Data masking techniques fall into two primary categories: static and dynamic. Static data masking applies masking rules to data prior to storage or sharing, making it ideal for protecting sensitive data that is unlikely to change over time. With dynamic data masking, masking rules are applied when the data is queried or transferred. 

Data tokenization

Data tokenization replaces sensitive data with a nonsensitive substitute, or token. These tokens are randomly generated data strings with no real meaning or value on their own. Since only the system that generated the token can access the data in its original form, sensitive data that has been tokenized can’t be reverse-engineered. 


Pseudonymization replaces private identifiers such as names or email addresses with fictitious ones. This technique preserves data integrity and ensures that data remains statistically accurate, which is an important consideration when using data for model training, testing and analytics. Unlike many other data anonymization techniques, pseudonymization doesn’t address indirect identifiers such as age, geographic location or location that can be used to identify specific individuals when combined with other information. This means data protected using this approach remains subject to GDPR data privacy regulations.

Data swapping

Data swapping reorders the data set attribute values so they no longer resemble the original data. By reordering data within database rows, this data anonymization method preserves the statistical relevance of the data while minimizing the re-identification risks.

Synthetic data 

Synthetic data addresses data privacy concerns in a way that’s unique among the other methods discussed here. Synthetic data is artificially produced with no traceable connection to any actual data record. Although synthetic data is machine-generated, it is a realistic representation of the original data set and can be used for similar purposes, minus the data privacy concerns. 

Anonymize Your Sensitive Data with Snowflake 

The Snowflake Data Cloud offers powerful security features that enable businesses to protect their sensitive data. With a multitude of features — such as dynamic data masking and end-to-end encryption for data in transit and at rest — organizations can collect, use and share sensitive data securely. 

In addition, Snowflake provides support for ITAR compliance, SOC 2 Type 2, PCI DSS compliance and support for HITRUST compliance. Snowflake’s government deployments have achieved FedRAMP ATO at the Moderate level.