Anonymization Techniques

Anonymization techniques refer to the methods used to protect the privacy of individuals by removing or obscuring their personal identifying information from datasets. These techniques are commonly used in data science and artificial intelligence to ensure that sensitive information is not disclosed or misused. Anonymization can be achieved through various methods such as generalization, suppression, perturbation, and encryption. Generalization involves replacing specific values with a more general value, such as replacing an exact age with an age range. Suppression involves removing certain data points entirely. Perturbation involves adding random noise to the data to make it more difficult to identify individuals. Encryption involves transforming the data into a coded format that can only be accessed with a key. Anonymization techniques are important for protecting the privacy of individuals while still allowing for the analysis of large datasets.
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