Data is regarded as the most vital part of modern existence.
Researchers and data scientists are frequently confronted with scenarios in which they either lack access to genuine data or are unable to utilize it owing to confidentiality or privacy concerns. The usage of mimic data or intentionally manufactured data is an excellent solution to this problem.
Synthetic data is data that is manufactured artificially or manually rather than by real-world events. Synthetic data comes to the rescue when there is a dearth of data for testing or when privacy is a top consideration.
In the medical or health sector, health records require privacy and confidentiality in such cases we use synthetic data for testing.
Machine learning with synthetic data
Machine learning algorithms require a good amount of data to be processed in order to create a reliable model.
Generating such a huge amount of data would be very difficult, these limited data will affect the performance of the neural network or machine learning models. However, using synthetic data makes it a lot easier. It can be quite useful in domains like Computer Vision and Image Processing, where model construction becomes much easier after synthetic data is produced. Generative Adversarial Networks (GANs) was one of the latest, most advanced applications of synthetic data, It was famous due to its image processing ability using machine learning.
While discriminator networks target to detect and predict the real data out of synthetic ones.
These networks keep learning and improving by building new nodes and layers.
Generative Adversarial Networks (GANs) were introduced recently and are a breakthrough in the field of image recognition.
A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow.
GAN composed two neural networks, one discriminator and one generator.
The generator network is used to generate synthetic images that are much closer to real-world images.
Types of Synthetic Data
The synthetic data is generated at random with the goal of concealing sensitive personal information while retaining statistical information from the original data. Synthetic data can be categorized into three types:
Fully synthetic Data
The data is entirely generated and contains no elements from the original. The density function of features in the real data is normally identified by the data generator for this type. Bootstrap approaches and multiple imputations are two common ways of generating fully synthetic data. This technique has high privacy protection with a fallback on the data's truthfulness because the data is totally generated and no real data exists.
Partially Synthetic Data
This data only replaces the values of a few sensitive features with synthetic values. In this case, the real values are replaced only if there is a high risk of disclosure. Multiple imputation and model-based techniques are two methods for generating partially synthetic data.
Hybrid Synthetic Data
This data is created by combining real and synthetic data. Each random record of real data is paired with a close record from synthetic data, and the two are then combined to form hybrid data.
Challenges in synthetic data
Synthetic data has strong roots in AI and ML, therefore it is also facing some challenges in the present industry.
- Several inconsistencies were discovered while attempting to replicate the complexities of real data to synthetic data.
- Synthetic data is skewed in its behavior due to its flexibility.
- Validating it with synthetic test data might not be enough for users. They might require you to validate it with real data.
- Many users may not accept synthetic data to be valid.
- Replicating all necessary features from real data might become complex.
- Difficulty in generating synthetic data.