Data Anonymization

This is where data anonymization comes into play, protecting confidential information by removing or encrypting individual identifiers. By allowing risk-free data sharing among digital media platforms, it drives more interesting offers to consumers.

What is Data Anonymization?

When sensitive and/or personal information is stored in company databases (often associating usernames with legal names, telephone numbers, social security numbers, emails, and street addresses), there’s always a risk of leaks. Even the most secure software can be attacked by hackers through unforeseen loopholes and malicious back doors.

However, running this information through an anonymization process avoids source identification. New data protection regulations are coming into force worldwide, like the General Data Protection Regulations (GDPR) in Europe, allowing businesses to collect anonymized data without requiring individual consent. All that’s needed is to strip away the data identifiers. More recent legislation includes the LGPD in Brazil and POPI in South Africa, with similar laws enacted in some U.S. states.

Data Anonymization Techniques

There’s more than one way to anonymize data. Depending on the planned purposes and the systems that will be using this information, current de-identification technologies include:

• Pseudonymization: it replaces private identifiers with pseudonyms (like randomly changing every Miller, for example, to Baker, Cooper, Porter, or Smith), while maintaining full data integrity and accuracy, which is an ideal solution for testing and training purposes;
• Generalization: it lessens identifiability by altering details (like apartment numbers or towns), while the information still remains accurate enough for their planned purposes;
• Data scrambling: it is a widely used technique that shuffles the digits or letters in an attribute irreversibly, so the data can never be matched back to their original records;
• Data masking: it hides data through altered values in duplicate databases where modification techniques can be used, like character and word replacement, encryption, and shuffling, although unsuitable for reverse engineering;
• Data blurring: it reduces information accurately, as this approximation technique alters values just enough to minimize the chances of identification, thus ensuring individual anonymity;
• Data swaps: these alter attribute values (names, birthdates) in datasets, which then fail to match up with the original records, ensuring more effective database anonymization;
• Data encryption: it is perhaps the most effective data anonymization technique, transforming personal information into unreadable coded form that is password-protected, retrievable in its original format only by approved users.