Synthetic Data Generation: Types, Techniques & More

Researchers and data scientists often encounter circumstances in which they either do not have the actual data or are unable to use it owing to confidentiality or privacy considerations.

To address this issue, synthetic data production is used to produce a replacement for genuine data.

The appropriate replacement of genuine data is required for the algorithm to perform properly, which should also be realistic in character. You can use such data for maintaining privacy, testing systems, or producing training data for machine learning algorithms.

Let’s explore synthetic data generation in detail and see why they’re vital in the age of AI.

What is Synthetic Data?

Synthetic data is annotated data generated by computer simulations or algorithms as a substitute for real-world data. It is an artificial intelligence-generated replica of actual data.

One may use data patterns and dimensions using advanced AI algorithms. They can create a limitless quantity of synthetic data that is statistically representative of the original training data once they’re trained.

There are a variety of approaches and technologies that can help us create synthetic data and you can use in a variety of applications.

Data generation software often requires:

• Metadata of a data repository, for which synthetic data must be created.
• Technique for generating plausible but fictional values. Examples include value lists and regular expressions.
• Comprehensive awareness of all data relationships, those declared at the database level as well as those controlled at the application code level.

It is equally necessary to validate the model and compare the behavioral aspects of real data to those generated by the model.

These fictitious datasets have all of the value of the real thing, but none of the sensitive data. It’s like a luscious, calorie-free cake. It accurately depicts the actual world.

As a result, you can use it to replace real-world data.

Importance of Synthetic Data

Synthetic data has characteristics to fit certain demands or situations that would otherwise be unavailable in real-world data. When there is a paucity of data for testing or when privacy is a top consideration, it comes to the rescue.

AI-generated datasets are adaptable, secure, and easy to store, exchange, and discard. The data synthesis technique is appropriate for subsetting and improving the original data.

As a consequence, it is ideal for use as test data and AI training data.

• To teach ML-based Uber and Tesla self-driving automobiles.
• In the medical and healthcare industries, to assess specific illnesses and circumstances for which genuine data does not exist.
• Fraud detection and protection are crucial in the financial sector. By using it, you may investigate new fraudulent instances.
• Amazon is training Alexa’s language system using synthetic data.
• American Express is using synthetic financial data to improve fraud detection.

Types of Synthetic Data

Synthetic data is created at random with the intention of concealing sensitive private information while keeping statistical information about characteristics in the original data.

It is mainly of three types:

• Fully synthetic data
• Partially synthetic data
• Hybrid synthetic data

1. Fully Synthetic Data

This data is entirely generated and contains no original data.

Typically, the data generator for this kind will identify density functions of features in real data and estimate their parameters. Later, from predicted density functions, privacy-protected series are created at random for each feature.

If just a few characteristics of actual data are chosen to be replaced with it, the protected series of these features are mapped to the remaining features of the real data to rank the protected and real series in the same order.

Bootstrap techniques and multiple imputations are two traditional methods for producing completely synthetic data.

Because the data is entirely synthetic and no real data exists, this strategy provides excellent privacy protection with a reliance on the data’s truthfulness.

2. Partially Synthetic Data

This data only uses synthetic values to replace the values of a few sensitive features.

In this situation, genuine values are only changed if there is a substantial danger of exposure. This change is done to protect the privacy of freshly created data.

Multiple imputation and model-based approaches are used to produce partially synthetic data. These methods can also be used to fill in missing values in real-world data.

3. Hybrid Synthetic Data

Hybrid synthetic data includes both actual and fake data.

A near-record in it is picked for each random record of real data, and the two are then joined to generate hybrid data. It has the benefits of both completely synthetic and partially synthetic data.

It therefore offers strong privacy preservation with high utility when compared to the other two, but at the cost of more memory and processing time.

Techniques of Synthetic Data Generation

For many years, the concept of machine-crafted data has been popular. Now it is maturing.

Here are some of the techniques used to generate synthetic data:

1. Based on distribution

In case no real data exists, but the data analyst has a thorough idea of how the dataset distribution would appear; they can produce a random sample of any distribution, including Normal, Exponential, Chi-square, t, lognormal, and Uniform.

The value of synthetic data in this method varies depending on the analyst’s level of understanding about a certain data environment.

2. Real-world data into known distribution

Businesses can produce it by identifying the best fit distributions for given real data if there is real data.

Businesses can use the Monte Carlo approach to produce it if they wish to fit real data into a known distribution and know the distribution parameters.

Though the Monte Carlo approach can help businesses in locating the greatest match available, the best fit may not be of enough use for the company’s synthetic data needs.

Businesses might explore employing machine learning models to suit distributions in these circumstances.

Machine learning techniques, such as decision trees, enable organizations to model non-classical distributions, which might be multi-modal and lack common properties of recognized distributions.

Businesses may produce synthetic data that connects to genuine data using this machine learning fitted distribution.

However, machine learning models are susceptible to overfitting, which causes them to fail to match fresh data or predict future observations.

3. Deep Learning

Deep generative models like the Variational Autoencoder (VAE) and the Generative Adversarial Network (GAN) can produce synthetic data.

Variational Autoencoder

VAE is an unsupervised approach in which the encoder compresses the original dataset and sends data to the decoder.

The decoder then produces output that is a representation of the original dataset.

Teaching the system involves maximizing the correlation between input and output data.

Generative Adversarial Network

The GAN model iteratively trains the model using two networks, the generator, and the discriminator.

The generator creates a synthetic dataset from a set of random sample data.

Discriminator compares synthetically created data to a real dataset using pre-defined conditions.

Synthetic Data Providers

Structured Data

The platforms mentioned below provide synthetic data derived from tabular data.

It replicates real-world data kept in tables and can be used for behavioral, predictive, or transactional analysis.

• Instill AI: It is a provider of a synthetic data creation system that uses Generative Adversarial Networks and differential privacy.
• Betterdata: It is a provider of a privacy-preserving synthetic data solution for AI, data sharing, and product development.
• Divepale: It is the provider of Geminai, a system for creating ‘twin’ datasets with the same statistical features as the original data.

Unstructured Data

The platforms mentioned below operate with unstructured data, providing synthetic data goods and services for training vision and reconnaissance algorithms.

• Datagen: It provides 3D simulated training data for Visual AI learning and development.
• Neurolabs: Neurolabs is a provider of a computer vision synthetic data platform.
• Parallel domain: It is a provider of a synthetic data platform for autonomous system training and testing use cases.
• Cognata: It is a simulation supplier for ADAS and autonomous vehicle developers.
• Bifrost: It provides synthetic data APIs for creating 3D environments.

Challenges

It has a long history in Artificial Intelligence, and while it has many advantages, it also has significant drawbacks that you need to address while working with synthetic data.

Here are some of them:

• A lot of errors may be there while copying the complexity from actual data to synthetic data.
• The malleable nature of it leads to biases in its behavior.
• There may be some hidden flaws in the performance of algorithms trained using simplified representations of synthetic data that have recently surfaced while dealing with actual data.
• Replicating all relevant attributes from real-world data can become complicated. It is also possible that some essential aspects may be overlooked throughout this operation.

Conclusion

The production of synthetic data is clearly piquing people’s attention.

This method may not be a one-size-fits-all answer for all data-generating cases.

Besides, the technique may require intelligence via AI/ML and be able to handle real-world complicated situations of creating inter-related data, ideally data suitable to a certain domain.

Nonetheless, it is an innovative technology that fills a gap where other privacy-enabling technologies fall short.

Today, synthetic data production may need the coexistence of data masking.

In the future, there may be greater convergence between the two, resulting in a more comprehensive data-generating solution.

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