What is Data Anonymization?
Anonymized data refers to a form of information sanitization where data anonymization tools remove personally identifiable information from data sets in order to preserve a data subject’s privacy. This helps to reduce the possibility of accidental disclosure when information is transferred across borders and allows for evaluation and analysis after anonymization.
The General Data Protection Regulation (GDPR), of the European Union, requires that stored data on EU citizens be anonymized or pseudonymized. Anonymized data sets are not considered personal information and therefore are not subject to GDPR. This allows businesses to use the data for a wider range of purposes without violating the data anonymization policy or data protection rights. Anonymized HIPAA data is an integral part of the healthcare industry’s commitment to patient privacy.
Data Anonymization Techniques
Data anonymization algorithms automate the process of protecting an individual’s identity from a data set. Some data anonymization methods include:
- Generalization – This is a way to reduce the identifiable data but retain accuracy.
- Perturbation – Modifies a dataset slightly by applying random noise and techniques that round numbers.
- Pseudonymization – Replaces private identifiers with fake identifiers/ pseudonyms – anonymization vs pseudonymization can be interchangeably
- Scrambling – The letters are mixed thoroughly and rearranged.
Shaping is also known as data swapping or permutation — it swaps and rearranges the attributes of datasets.
- Synthetic Data – Creates artificial data rather than altering the original dataset.
The complexity of the project as well as the programming language used will determine which data anonymization tool is best. Students conducting a survey may have different needs than a bank customer transaction analyst.
Professional data anonymization software must comply with GDPR anonymized data and provide interactivity capabilities that allow analysts to query data dynamically through an interface with a one-time initial setup. One of the most used languages for anonymizing personal data is R anonymize.
Anonymized Data vs De-identification
De-identification refers to the removal of all personally identifiable information. This prevents an individual’s identity from being compromised. Pseudonymization is a common method of de-identification. It involves removing all personal identifiers from data files and replacing them with temporary IDs.
Data anonymization is also used to de-identify metadata and general data about identification. Data anonymization does not prevent future re-identification of data controllers, but de-identification can preserve information that could be re-linked by trusted parties in certain circumstances.
Data Anonymization Best Practices
Multiple layers of protection are the best way to anonymize. Particularly in the case of Big Data Analytics where one layer of anonymization may not be sufficient. The following security measures can be used to add layers of protection against de-anonymization attacks.
- Database activity monitoring gives real-time alerts about policy violations in data warehouses.
- Database firewalls block SQL injections by evaluating known vulnerabilities.
- Data classification and data discovery identify the context and quantity of data stored on-premises or in the cloud.
- Software that detects data loss can inspect sensitive information in motion and at rest to identify potential data breaches.
- Data masking can render sensitive data inaccessible to the wrong people.
- Machine learning is used to analyze user behavior in order to establish baseline data access behavior and detect anomalies.
- An integrated user rights management feature detects and monitors data access and privilege user activity and flags any inappropriate privileges.
Data Masking vs Anonymization
Data masking is the deliberate creation of unique but unauthentic versions of personal user information using encryption and data shuffling techniques. This masks personal data but preserves the unique characteristics of the data. Testing on the masked data will produce the same results as testing done on the original data set.
Data masking is a way to add security to data anonymization. It masks certain data pieces and shows only the most important data to data handlers who have been authorized to see them. This allows for safe application testing, where only authorized testers can see what they need.