Data Masking and Synthetic

Data Masking

Data masking is a way to create a fake, but a realistic version of your organizational data. The goal is to protect sensitive data, while providing a functional alternative when real data is not needed—for example, in user training, sales demos, or software testing.

Data masking processes change the values of the data while using the same format. The goal is to create a version that cannot be deciphered or reverse engineered. There are several ways to alter the data, including character shuffling, word or character substitution, and encryption.

Data masking Benefits

  • Minimizes the exposure of sensitive data by masking it to non-privileged users, including database administrators and developers who require access to the database.
  • Enables organizations to specify how much data they want to reveal or restrict access, allowing them to turn this information into the server quickly.
  • Supports database configuration to conceal sensitive data in query results without changing the data in the database.
  • Applies masking rules in query result sets, making DDM easy to implement in existing applications.
  • Provides both partial and full masking options and allows you to protect numeric data with random masking functions.
  • Helps enforce data privacy standards to maintain regulatory compliance.
  • Provides transparency to applications, with masking applied based on user privileges.
  • Provides agility, masking data on the fly while keeping underlying data intact in the database.

Synthetic Data

Synthetic data is information that’s artificially generated rather than produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data.

Why is synthetic data important now?

Synthetic data is important because it can be generated to meet specific needs or conditions that are not available in existing (real) data. This can be useful in numerous cases such as

  • When privacy requirements limit data availability or how it can be used
  • Data is needed for testing a product to be released however such data either does not exist or is not available to the testers
  • Training data is needed for machine learning algorithms. However, especially in the case of self-driving cars, such data is expensive to generate in real life

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