Anonymization is the approach of cleaning or masking identifiers where addresses can not be known on individual-level to data records. In an era where data is collected and processed en masse, this technique plays a vital role in maintaining privacy.
Why is it Important to Anonymise?
Ensure Privacy: Anonymization ensures that personal identifiers cannot be discerned.
Improved data security: With the use of deidentification or data anonymization technique you are eliminating the risk of data breaches and unregulated access by removing personally identifiable information.
Compliance: Anonymization allows organizations to comply with data privacy regulations such as GDPR and CCPA.
Techniques for Anonymization
There are three ways to anonymize data:
Data Masking:
Very much in the same theme as Substituted Values, substitution means doing exactly what it says.
Such as full names with initials and actual addresses with just a city.
Data Aggregation:
Aggregating one or more data records into a single combined record
This might be done by aggregating the data based on some characteristics such as the age group or the location.
Data Perturbation:
Add some minor noise to the data-to make the data less accurate but still suitable for analysis purposes.
This can be adding or subtracting a small delta to numerical data or swapping characters in text data.
k-Anonymity:
It is a technique that guarantees that no combination of k or fewer attributes can identify each record uniquely in a given dataset.
l-Diversity:
A method that guarantees that every group of records with the same value for sensitive attribute contains at least l different values of that attribute.
t-Closeness:
A method that provides that the distribution of sensitive attributes is similar in any group of records to the distribution of those attributes in the entire dataset.
