Data Are Made Anonymous By Anonymization
Anonymization is a data processing technique that removes or modifies personally identifiable information so that the resulting data cannot be used to identify an individual—effectively ‘anonymizing’ it.
Anonymization is typically used in situations where the data holder needs to share or publish a dataset while still preserving the privacy of the individuals who are included in it. This can be done by implementing a number of anonymization techniques, such as:
- Data masking: This technique replaces or modifies sensitive information with dummy data. For example, a dataset containing patient names can be masked by replacing them with a pseudonym (fake name).
- Encryption: Data encryption is the process of transforming data into a form that is unreadable to anyone without the encryption key.
- Hiding: Another anonymization technique is to simply hide the personally identifiable data. This could involve removing names, locations, and other identifying information from the dataset.
Anonymization is an important step in data privacy, as it helps protect the identity of individuals included in a dataset. By anonymizing data, organizations can still use the data for its intended purpose without having to worry about leaking any personal information.