Synthetic data generation refers to the process of generating artificial or simulated data that resembles real data, but it will not have any personal or sensitive information. It involves generating data that will follow statistical properties, patterns and various relationships found in the original dataset, allowing it to be used for various purposes without compromising privacy or security.

Synthetic data generation using generative AI techniques has become an increasingly popular approach in various domains, including computer vision, natural language processing, and data science. Generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be utilized to create synthetic data that closely resembles real data while preserving privacy and confidentiality.

Generative Adversarial Networks (GANs) are a popular class of Generative AI models used for synthetic data generation. They usually consist of two neural networks: a Generator and a Discriminator.  The generator will generate synthetic data while the discriminator will try and distinguish the data between real and synthetic. Through extensive training, both models attain maturity and improve their performance resulting in generation of high-quality synthetic data that is very close to real data.

Variational Autoencoders (VAEs) are another type of Generative AI model used for synthetic data generation. In this model, the concept of auto-encoders is used which consist of an encoder network and a decoder network. By sampling the data fed into the encoder which will encode via lossy encoding or lossless encoding. In lossy encoding, some property is lost during reduction of dimensions, and this is irreversible. Data is lost forever and cannot be recovered at a later point of time. In lossless encoding, no information is lost during the reduction of dimensions.

In Machine learning dimension reduction refers to the way of reducing the number of properties that define a data. The reduction is done by either selecting few properties or by extraction of the same.

High-level Process

The high-level process of synthetic data generation using Generative AI techniques can be depicted using the below diagram:

• Data Collection: As the first step, a sample dataset is collected that will represent the real-world examples of the target dataset. This dataset acts as the foundation for the Generative AI model to learn and evolve. This can involve manual or automated ways of segregating the dataset by carefully removing the personal identifiable information or any sensitive information.
• Model Training: The Generative AI models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are trained using the data collected. The model learns the underlying patterns, structures, and statistical distribution of properties present in the real data. The larger the dataset provided for learning, the better trained model it will become.
• Generating Synthetic Data: Once the model has been trained sufficiently with sample data collected, it can now begin to generate synthetic data using specific input to the model. The generated data can resemble the pattern and characteristics of the original dataset.
• Data Augmentation: Data augmentation is the process of augmenting the original dataset with the generated synthetic data. This augmented data can now be fed to Machine Learning models for their training purpose.
• Privacy Preservation: Synthetic data generation can help protect sensitive or personally identifiable information (PII). By generating synthetic data that does not contain any real individual’s personal information, privacy risks can be mitigated when sharing or using the data for testing purposes.

Applications

Synthetic data generation has several applications across different domains, including:

• PII data protection : Synthetic data generation enables researchers to share or publish data for analysis without violating privacy regulations or disclosing sensitive information. Synthetic data can be used to perform experiments, develop models, or validate hypotheses without compromising individuals’ privacy.
• Machine learning and AI: Synthetic data can be used to augment training datasets, especially when the original dataset is small or lacks diversity. It helps to improve the performance and reliability of machine learning models by providing additional data points that capture different scenarios or edge cases.
• Testing: Synthetic data can be used to test and validate data processing pipelines, algorithms, and software applications. It allows developers to generate specific scenarios or test cases that may not be easily available in the original dataset.
• Data augmentation: Synthetic data can be used to expand the size and diversity of datasets for various applications, such as computer vision or natural language processing. By generating additional data points, it helps improve the generalization and performance of models.
• Anonymization and data sharing: Synthetic data can be used as a substitute for sensitive or confidential data, allowing organizations to share datasets with external parties while protecting individual privacy. Synthetic data can maintain the statistical properties and relationships of the original data while preventing re-identification of individuals.
• Algorithm Development: Synthetic data can be employed to develop and benchmark new algorithms. By generating datasets with known characteristics and ground truth labels, researchers can compare the performance of different algorithms, evaluate their strengths and weaknesses, and establish benchmark datasets for specific tasks.

It is worth mentioning that while synthetic data can closely resemble real data, it may not capture all the intricate details and complexities present in the original dataset. Therefore, thorough evaluation and testing are essential to ensure that the synthetic data sufficiently represents the real-world scenarios it aims to mimic. Furthermore, the specific implementation details and choice of generative AI model may vary depending on the task, dataset, and requirements of the application. Different variations of GANs, VAEs, or other generative models might be more suitable for different scenarios.