What strategy involves hiding data in a data set to prevent someone from identifying specific individuals based on other data fields present?
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With data anonymization, data is manipulated in such a way so as to prevent the identification of an individual through various data objects, and is often used in conjunction with other concepts such as masking.
The strategy that involves hiding data in a data set to prevent someone from identifying specific individuals based on other data fields present is known as anonymization.
Anonymization is the process of removing or obfuscating identifying information from data sets, such as personal or sensitive data, to prevent individuals from being identified or re-identified. This is accomplished by altering or removing data fields that could be used to link specific individuals to their personal information. Anonymization is commonly used in situations where data needs to be shared or analyzed while preserving the privacy and confidentiality of individuals.
Tokenization, on the other hand, is a process of substituting sensitive data with a non-sensitive equivalent, known as a token. This token can be used in place of the sensitive data for certain purposes without revealing the original data.
Masking is a technique of replacing sensitive data with characters or symbols, such as asterisks, to hide the original data from view.
Obfuscation is a process of making data more difficult to understand or interpret, often by using techniques such as encryption, scrambling, or randomization.
Therefore, while tokenization, masking, and obfuscation can all be used to protect sensitive data, anonymization specifically focuses on removing identifying information to prevent individuals from being linked to their personal information.