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Data anonymization is the process of erasing or encrypting sensitive information within a subset of data. Data anonymization is an operation that is generally done when it’s necessary to process or share information that, for example, might contain data that can be traced back to specific users. This sensitive information is called Personally Identifiable Information (PII) and can include names, credit card information, personal addresses, etc. Another example might be the medical data used for research purposes, but first needs to be anonymized to protect the patients’ identity.
Generally, a data anonymization process aims to use the data without compromising sensitive information to be seen by unauthorized parties. This process can be done in many ways, as it can be a permutation of the sensitive variables, encryption, generalization, permutation, and aggregation. In this way, any trace of sensitive information is altered so that the original data can no longer be recovered.
Examples of Data Anonymization Scenarios:
- Performing anonymization of credit card transactional data to be used to train a machine learning model for fraud prediction.
- Storing anonymized data from users to comply with GDPR (General Data Protection Regulation).
The data is valuable to companies as it allows them to understand user behavior and provide meaningful insights. Therefore, the company needs to effectively capture and anonymize the data without leaving any traces that can be used to trace it back to the source.
The Data Anonymization process must ensure that the information is encrypted correctly and clean from any identifiers. There might be a case where attackers reconstruct the original data by cross-referencing multiple sources to reveal personal data. Therefore, the process applied to protect the sensitive data must ensure that the data has been irreversibly changed and can not be reconstructed by any of the parties involved.
Data Anonymization allows the data to be used and transferred securely to obtain insight for decision making, business analytics, or train machine learning models, as mentioned before, reducing the risk of potential unintended use while complying with data protection regulations. It also reduces potential risks as anonymized stored information doesn’t require a different level of security measures. It would be necessary if the sensitive information would still be present within the data. It also allows easier compliance, as the anonymized data can be retained for more extended periods.