Description: Linear regression anonymization is a method that uses linear regression techniques to anonymize data, ensuring that sensitive information cannot be linked to specific individuals. This approach is based on the idea that predictive models can be created that, when applied to datasets, allow for the removal or modification of characteristics that could identify a person. Linear regression, at its core, seeks to establish a relationship between variables, and in the context of anonymization, it is used to transform the original data into a form that preserves analytical utility while concealing the identity of subjects. This method is particularly relevant in the field of data protection, where privacy is an increasing concern. By applying linear regression, synthetic data can be generated that maintains the statistical properties of the original dataset, allowing organizations to perform analyses without compromising the confidentiality of personal information. Linear regression anonymization presents an effective solution for complying with data protection regulations, such as GDPR, by enabling the use of data for research and analysis without the risk of re-identification.
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