concepts

Definitions of the terms used in this project

Two main strategies are used:

• Dynamic Masking offers an altered view of the real data without modifying it. Some users may only read the masked data, others may access the authentic version.

• Permanent Destruction is the definitive action of substituting the sensitive information with uncorrelated data. Once processed, the authentic data cannot be retrieved.

The data can be altered with several techniques:

• Deletion or Nullification simply removes data.

• Static Substitution consistently replaces the data with a generic value. For instance: replacing all values of a TEXT column with the value “CONFIDENTIAL”.

• Variance is the action of “shifting” dates and numeric values. For example, by applying a +/- 10% variance to a salary column, the dataset will remain meaningful.

• Generalization reduces the accuracy of the data by replacing it with a range of values. Instead of saying “Bob is 28 years old”, you can say “Bob is between 20 and 30 years old”. This is useful for analytics because the data remains true.

• Shuffling mixes values within the same columns. This method is open to being reversed if the shuffling algorithm can be deciphered.

• Randomization replaces sensitive data with random-but-plausible values. The goal is to avoid any identification from the data record while remaining suitable for testing, data analysis and data processing.

• Partial scrambling is similar to static substitution but leaves out some part of the data. For instance : a credit card number can be replaced by ‘40XX XXXX XXXX XX96’

• Custom rules are designed to alter data following specific needs. For instance, randomizing simultaneously a zipcode and a city name while keeping them coherent.

• Pseudonymization is a way to protect personal information by hiding it using additional information. Encryption and Hashing are two examples of pseudonymization techniques. However a pseudonymizated data is still linked to the original data.