Synthetic Data

TL;DR

Artificially generated datasets that imitate real-world data characteristics.
Commonly used to test and augment machine learning models when real data is limited or unavailable.
Used to protect privacy by replacing or de-identifying sensitive records.

Definition

Synthetic data is artificially generated data that is designed to mimic real-world data.

Explanation

Synthetic data is created to serve multiple purposes without relying on original sensitive or hard-to-obtain data. Typical uses include testing and validating machine learning models, augmenting limited datasets to improve model training, and protecting the privacy of sensitive information by substituting or de-identifying real records. Proper design and evaluation of synthetic datasets are necessary to ensure they are representative and accurate for their intended purpose.

Examples

Testing machine learning models

Machine learning models are only as good as the data they are trained on. If the training data is biased, incomplete, or inaccurate, the model will likely perform poorly when applied to real-world situations. To ensure that a machine learning model is robust and generalizable, it is important to test it on a diverse and representative data set. However, it is not always possible or ethical to obtain large amounts of real-world data for testing purposes. This is where synthetic data can come in handy. By generating synthetic data that is similar to real-world data, but not identical, machine learning practitioners can test their models on a wide range of scenarios without the need to collect and handle real data.

Protecting sensitive data

In many cases, it is important to protect the privacy of sensitive data, such as medical records, financial transactions, or personal identification information. Synthetic data can be used to replace real data in these cases, allowing researchers and analysts to work with realistic data sets without exposing sensitive information. For example, a healthcare company might use synthetic data to train a machine learning model to predict patient outcomes without exposing actual patient data. Synthetic data can also be used to de-identify data sets by replacing sensitive information with artificially generated data, making it more difficult for unauthorized parties to re-identify individual records.

Notes or pitfalls

It is important to carefully design and evaluate synthetic data sets to ensure that they are representative and accurate enough for the intended purpose.

Machine learning
De-identification
Data augmentation
Privacy