data anonymization

What is data anonymization?

Data anonymization describes various techniques to remove or block data containing personally identifiable information (PII). Data anonymization promotes data privacy while maintaining the integrity and usefulness of the overall data set.

This approach supports analysis and research without revealing the identity of any subjects involved. For example, a drug trial wants all data about a new pharmaceutical's impact, but does not need to know the names of individual patients. Data anonymization uses one of several approaches to halt access to patients' PII while still enabling researchers to benefit from the clinical data. If, in another case, a cybersecurity incident causes a breach, anonymized data helps users stay safe by ensuring their PII has been isolated from the compromised data.

Types of data anonymization techniques

Data anonymization involves various techniques to ensure personal data cannot be associated with an individual. The most common types include the following:

• Data masking. By hiding or altering values in a data set, data masking leaves the data usable, but the original values cannot be identified or reverse-engineered.
• Pseudonymization. This technique replaces private identifiers with false identifiers, or pseudonyms, which maintain data confidentiality and statistical accuracy while preventing direct identification.
• Generalization. This data anonymization technique involves removing some parts of the data or replacing it with more general information to make it less identifiable.
• Data swapping or data shuffling. This technique rearranges data set attribute values so that they do not match the original data.
• Data perturbation. This involves slightly modifying the data set by adding random noise or applying rounding techniques to the data.
• Synthetic data. Very different from the other techniques listed, with synthetic data, artificial data sets are created algorithmically, leaving them without direct relation to actual individuals.

Advantages of data anonymization

Data anonymization provides organizations with several advantages over non-anonymized data. Following are some of the key benefits:

• Privacy protection. The most basic and primary advantage of data anonymization is its ability to protect PII and individual privacy.
• Regulatory compliance. Multiple privacy regulations, including the General Data Protection Regulation in the European Union and the Health Insurance Portability and Accountability Act in the United States, require data anonymization.
• Reduced data security risk. In a data breach, data anonymization reduces the attack's impact on individuals.
• Fast and protected data sharing. Anonymized data can be shared more freely for analysis between departments within an organization -- or with third parties -- without compromising individual privacy.
• Support for research and analysis. Even without PII, anonymized data remains valuable for research and analysis. For example, in healthcare, anonymized patient data is used to study public health trends without compromising patient confidentiality.

Disadvantages of data anonymization

Despite its benefits, data anonymization brings challenges. The disadvantages that organizations need to consider include the following:

• Potential de-anonymization. Risk remains that anonymized data could be de-anonymized, unmasked or inferred using different techniques.
• Data utility loss. Because sensitive or unique data points are removed or obfuscated, anonymization can make it difficult to draw accurate insights from the data or use it for specific purposes that require detailed information.
• Resource strain. Often, data anonymization can be complex and resource-intensive to ensure privacy is maintained.
• Limitations for personalization. Anonymized data is not useful for personalizing targeted offers or services since the ability to connect insights with an individual is lost due to the removal of PII.

Examples of anonymized data

Anonymized data isn't just about protecting user privacy. It's also about maintaining useful data. Following are some industry vertical examples of how anonymized data is used effectively:

• Educational data. Student performance data is anonymized to study educational outcomes and teaching effectiveness.
• Healthcare data. Patient records are anonymized for research purposes. All PII details -- such as names and addresses -- are altered so that the data is not linked to individual patients. Researchers study health trends, disease patterns and treatment outcomes without endangering patient privacy.
• Financial data. Anonymizing personal identifiers from bank and credit card transaction data allows analysis of spending habits, detection of fraud patterns or assessment of credit risk without revealing customers' identities.
• Internet usage data. Companies anonymize search queries, browsing histories and online behavior data to improve products and services, such as search engine algorithms, without compromising user privacy.
• Marketing data. Consumer behavior data collected by digital agencies is anonymized to comply with privacy regulations, yet it continues to provide insights for personalized user experiences.
• Research data. Survey responses and other research data are anonymized while allowing researchers to analyze trends.
• Telecommunications data. Telecom companies anonymize call records, message logs and location data to study usage patterns, network performance or customer behavior.
• Transportation data. Data from public transport systems, such as travel times and route usage, is anonymized to improve services and infrastructure planning. Personal details such as names and payment information are removed so that individual travelers cannot be identified.