Data anonymization techniques modify data so that it can’t be linked to a specific person, while preserving its analytical and operational functionality.
Table of Contents
What are Data Anonymization Techniques?
Key Data Anonymization Techniques
Who Uses Data Anonymization Techniques?
Data Anonymization Techniques Based on Business Entities
What are Data Anonymization Techniques?
Data anonymization techniques are the methods employed to obscure sensitive or personal information in a dataset. Data anonymization makes it impossible to identify the individual behind the data, ensuring privacy compliance and security.
Any organization that collects, stores, handles, or transports data needs to use data anonymization tools in order to be compliant with privacy regulations such as CCPA/CCPR, GDPR, HIPAA, etc. How much anonymization is sufficient, and how should data be anonymized? This depends on both the business context and the nature of the data.
Certain data anonymization techniques are more suitable than others, for different types of data. For example, character masking might be best for hiding direct identifiers, while aggregation might work better for indirect identifiers. If the attribute value is continuous, techniques like data perturbation may be most appropriate. For discrete values (like yes/no answers), other techniques may be preferable.
Also, data stakeholders need to keep in mind that data anonymization techniques may modify data in significantly different ways. Some change only parts of an attribute (like character masking) and some (like aggregation) actually replace an attribute’s value across multiple records. Some techniques replace an entire attribute with unrelated, yet consistent, data (pseudonymization), whereas others remove the attribute entirely (attribute suppression). And some data anonymization techniques can even be used together – like suppressing or removing outlier records after generalization.
Key Data Anonymization Techniques
Here are 12 of the most common data anonymization techniques:
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Data redaction
This simple data anonymization technique simply removes or obscures confidential or classified values from a dataset, so it can be shared without compromising privacy or sensitive data. -
Data nulling
Another simple data anonymization technique, nulling simply deletes sensitive data from the dataset, replacing it with a series of NULL values or attributes instead. -
Data masking
Data masking basically hides data with altered values, making reverse engineering or detection impossible. Data masking techniques can create a mirror version of a database, or apply character shuffling, encryption, or word/character substitution. An example of this would be replacing a certain character with a symbol like an asterisk. -
Pseudonymization
Pseudonymization de-identifies data values by substituting private identifiers with fake identifiers or pseudonyms. An example would be replacing every instance of the name “Fred Jones” with “John Q. Public”. This method preserves the integrity and statistical accuracy of data, which enables it to be used for analytics, testing, training, and development – without compromising privacy. -
Generalization
Generalization purposely removes parts of a dataset to make it less identifiable. Using this technique, data is modified into a set of ranges with appropriate boundaries – thus removing identifiers while retaining data accuracy. For example, in an address, the house numbers could be removed, but not the street names. -
Data swapping
Also called data shuffling or data permutation, data swapping rearranges dataset attribute values so they no longer sync with the original values. This would include, for example, swapping attributes with identifiers like date of birth. -
Data perturbation
Perturbation changes the original dataset slightly by rounding numbers and adding random noise. An example of this would be using a base of 5 for rounding values like house number or age, since this leaves the data proportional to its original value. -
Data encryption
In the data masking vs encryption comparison, data encryption turns data into encrypted code that only approved users can decrypt. This data anonymization technique is a viable alternative to pseudonymized data, generally acceptable to regulators. -
Hashing
Hashing turns a certain key or string of characters into another value. It then uses functions or algorithms to map those values, so they are still discoverable without revealing the original data. It’s worth noting that hashing is unidirectional – meaning that hackers can’t trace hashed data back to the original source. -
Bucketing
Bucketing takes one distinguishing value – like a person’s first name – and turns that value into a generalized term, like <FIRSTNAME>. These values are grouped into smaller buckets so that the PII is removed, but the data can still be used for analysis. -
Tokenization
Tokenization replaces sensitive data (i.e., tokenizes it) with non-sensitive values. By way of example, tokenization would take a bank account number and swap it into a random string of characters. The actual bank account number remains securely stored, yet during transactions the tokenized data isn’t exposed. -
Synthetic data generation
Synthetic data generation is perhaps THE most advanced data anonymization technique. This method algorithmically creates data that has no connection to real data. It creates artificial datasets rather than altering or using an original dataset – which could risk privacy and security. For example, synthetic test data can be created using statistical models based on patterns in the original dataset – via standard deviations, medians, linear regression, or other statistical techniques.
Who Uses Data Anonymization Techniques?
Data anonymization techniques are applied in numerous industries and use cases, including:
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Utilities and energy: To gather usage-based insights without compromising customer privacy, data anonymization is essential.
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Healthcare: Data anonymization enables healthcare providers to conduct research without impinging upon patient privacy.
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Education: Didactic technologies produce valuable data about learning trends, yet data anonymization is crucial to safeguard student data and avoid personal identification.
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Financial services: The anonymization of data helps financial services companies comply with regulations like PCI DSS, yet still offer attractive customized products to specific segments.
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Telecom: Anonymized data allows telcos to understand crucial data about urban mobility (based on connections to cell towers) without revealing customer identity.
Data Anonymization Techniques Based on Business Entities
The most effective and technologically advanced methodology for anonymization of data relies on the entity-based data masking technology. A business entity corresponds to all the data associated with a specific device, customer, invoice, etc. Data relating to each business entity instance is stored and accessed from an individually encrypted Micro-Database™. Entity-based data anonymization leverages pre-determined business rules to offer organizations greater productivity, while still safeguarding compliance and individual privacy.
