Definition and Purpose of Anonymization in Big Data

Anonymization in big data refers to the process of transforming personal information in a way that the individual whom the data describes remains unidentifiable. This practice ensures privacy and protects sensitive information from misuse. By removing or encoding identifiers, anonymization seeks to balance data utility with privacy concerns. However, evaluating its effectiveness involves assessing both the robustness of anonymization techniques against re-identification attacks and the impact on the utility of the data.

Anonymization in big data refers to the process of transforming personal information in a way that the individual whom the data describes remains unidentifiable. This practice ensures privacy and protects sensitive information from misuse. By removing or encoding identifiers, anonymization seeks to balance data utility with privacy concerns. However, evaluating its effectiveness involves assessing both the robustness of anonymization techniques against re-identification attacks and the impact on the utility of the data.

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