BEST PRACTICES Ethical boundaries of data anonymization — challenges and reflections

Companies and institutions collect ever-larger amounts of information about customers, patients, or citizens, while expectations regarding its protection are also rising. Anonymization appears to be the golden mean – it allows the use of generalized data (e.g., for statistical purposes) while protecting individuals privacy. Let’s look at the ethical boundaries of anonymization: where technology meets responsibility, and good practices translate into user trust.

Anonymization is the process of transforming data so that it cannot be linked to a specific person. Two concepts should be distinguished: pseudonymization (replacement of identifiers, but reversible to identifiable data) and full anonymization (where returning to the original data is not possible).

Key elements of anonymization:

• Removing direct identifiers – such as first and last name, national ID number, phone number, or email address.
• Hiding or modifying indirect identifiers (quasi-identifiers) – e.g., postal code, date of birth, occupation.
• Hiding or masking sensitive data – information that could lead to privacy breaches or discrimination, e.g., medical or genetic data, biometrics.

Regulations like GDPR set the framework for data protection and formal requirements. However, the law often reacts slowly to rapid technological change. It establishes a minimum standard – a kind of “line of defense.” Ethics should be an additional filter that helps make decisions not only in terms of compliance but also in terms of social responsibility and trust. Organizations that limit themselves to literal compliance risk reputational loss. Those that prioritize ethics beyond regulation gain competitive advantage and long-term user loyalty.

Where the law lags behind ethics?

There is a lack of clearly defined technical standards. GDPR states that “anonymization should be irreversible,” but it does not specify precise methods. That leaves room for interpretation – and therefore for abuse.

• The illusion of anonymity – organizations may claim data are anonymous when, in reality, appropriate tools and processes have not been fully applied.
• Excessive distortion – to entirely eliminate re-identification risk, data are often altered to the point that they lose analytical value.
• The social trust aspect – users increasingly ask not only whether their data are legally protected, but whether the company genuinely cares about their privacy.

In addition to following generally accepted regulations, it is worth adopting several principles of ethical conduct:

• Data minimization – collecting only what is necessary.
  Transparency – organizations should clearly communicate how and why they anonymize data.
• Balance between privacy and data value – finding a proportion between security and business or scientific goals.
• Organizational responsibility and awareness – treating data carefully, as if they directly concerned the person to whom they relate.

Current solutions on the market, such as Redact, enable not only efficient information protection but also adjusting the level of anonymization to real needs.

In practice, this means companies can safely share data while minimizing the risk of disclosing sensitive information. Solutions like those offered by Redact show that security, regulatory compliance, and ethics can be combined – without compromising data utility.

Anonymization is a powerful tool, but it must not be treated as an absolute shield. What matters is a conscious approach – combining technology with ethics, law with responsibility, and security with transparency. Only then do data remain truly “anonymous” – not just in a technical sense, but also in a social one.