Meets GDPR requirements for pseudonymisation under the Data Protection by Design and by Default requirements.
CCPA compliant
As reported in 11 Limina reviews.
Meets de-identification requirements under the CCPA.
Functionality (3)
Static pseudonymization
As reported in 11 Limina reviews.
Offers traditional static de-identification (also known as consistent replacement), where the pseudonymized data uses the same pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox and the Robert Fox name is used multiple times. This type of pseudonymization carries some risks of re-identification if paired with enough datasets.
Dynamic pseudonymization
As reported in 11 Limina reviews.
Offers dynamic de-identification (also known as random replacement), where the pseudonymized data uses different pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox once, and then the next time the data is used the name changes to Michael Jones. This type of pseudonymization carries lesser risk of re-identification if paired with many datasets.
Batch de-identification
Based on 10 Limina reviews.
Offers methods to de-identify large volumes of data using batch files.
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