Data Generalization

Why Generalization?

Tokenization, a de-identification technique, is an effective way to protect primary identifiers. However, it leaves datasets susceptible to more sophisticated attacks, such as linkage attacks. In a linkage attack, quasi-identifiers are used to join datasets and form a richer combined dataset that can re-identify individuals or reveal unintended private information. Data generalization allows you to replace a data value with a less precise one via binning, reformatting, rounding or truncating, which preserves data utility and protects against linkage attacks.

Data Generalization Examples

33

Age

30 - 40

Age

12/07/1978

Date

1978

Date

Los Angeles

Location

California

Location

Apply K-Anonymity to Protect Against Data Linkage

In a linkage attack, quasi-identifiers are used to join datasets and form a richer combined dataset that can re-identify individuals or reveal unintended private information.K-anonymity applies generalization to ensure that the smallest number of indistinguishable individuals is a group of size ‘k’. That means every individual in a dataset is indistinguishable from at least k-1 others.

Maintain Control with Manual Data Generalization

With Manual Generalization, you define the data generalization rules. Privitar’s advanced capabilities allow you to set the k-anonymity threshold in accordance with your tolerance for linkage and re-identification risks for each specific data use. The Privitar Platform automatically drops rows that are in clusters that fall below thresholds so that you know k-anonymity is achieved.

Maximize Utility with Automatic Data Generalization

With Automatic Generalization, you define k-anonymity cluster size and allow the Privitar Platform to dynamically determine the data generalization rules to use. Privitar adjusts the blurring used to achieve k-anonymity for all quasi-identifiers – without the need to drop records. You maintain the greatest precision and include all of the data to achieve maximum data utility.

Maintain control and maximize utility with Privitar’s data generalization capabilities

Why Generalization?