What is synthetic data?: definition and benefits

What is synthetic data?

Synthetic data is artificially generated information that imitates the statistical properties of real-world data without containing actual real-world information. Created through algorithms or simulations, it helps in areas where real data is limited, sensitive, or hard to obtain.

Synthetic data is commonly used in training AI models to facilitate robust research and development by providing a privacy-compliant, scalable alternative to real datasets. Despite its benefits, creating accurate and unbiased synthetic data poses challenges, including maintaining realism and avoiding ethical pitfalls.

How is synthetic data generated? 

Synthetic data is generated using various techniques, each tailored to produce datasets that closely mimic the characteristics of real-world data.

The synthetic data generation process involves sophisticated algorithms and models that understand and replicate the patterns, trends, and correlations within the original data.

Here are some of the most common synthetic data generation methods: 

Generative models 

These models are typically algorithms trained to generate new data points. For example: 

• Generative Adversarial Networks (GANs): possess two neural and simultaneously trained networks, a generator and a discriminator. The generator produces synthetic data, while the discriminator evaluates its authenticity.

• Variational Autoencoders (VAEs): are a type of autoencoder that learns a compressed, dense representation of the data, and then generates new data based on this representation.

Simulation-based methods

These involve creating data based on simulations of real-world processes or systems. This approach is often used in domains where the underlying mechanisms of the system are well-understood, such as in engineering or environmental science.

Data augmentation

While primarily used to expand existing datasets rather than create them from scratch, data augmentation modifies real data points through techniques like cropping, rotating, or adding noise, to generate new, varied data points. This method is particularly common in image and signal processing tasks.

Rule-based data generation

For some applications, synthetic data can be generated through predefined rules. These rules are based on expert knowledge about the domain, creating data that follows expected patterns and distributions.

Synthetic minority over-sampling technique (SMOTE)

In machine learning, especially for dealing with imbalanced datasets, SMOTE is used to generate synthetic examples of the minority class to balance the dataset, improving the performance of classification algorithms.

Types of synthetic data

The types of synthetic data vary widely and are based on their nature and generation method. Synthetic data types can range from numerical data to images and text, each serving different purposes across various fields. Let’s see which are the most common synthetic data types.

Common applications of synthetic data

Synthetic data has a wide range of applications across various industries and research fields., It is quite applicable across contexts due to its ability to simulate real-world data overlooking the associated privacy concerns or the limitations of scarce datasets. Let’s dive into some of the real-world applications of synthetic data.

• Training machine learning models

One of the most prevalent uses of synthetic data is in training and testing machine learning and AI models, especially when real data is limited, sensitive, or expensive to collect. Synthetic data can help in creating robust models by providing diverse and extensive datasets.

• Data privacy and anonymization

Synthetic data enables the use of realistic data sets without compromising individual privacy, making it ideal for industries governed by strict data protection regulations, such as healthcare, finance, and education.

• Software testing and development

Developers use synthetic data to test new software, applications, and systems in a controlled environment. This allows for the identification and rectification of bugs and vulnerabilities without exposing real user data.

• Healthcare research

In the healthcare industry, synthetic data can simulate patient records, genetic information, and disease outbreaks, facilitating research and development while ensuring patient confidentiality is maintained.

• Financial modeling 

Banks and financial institutions utilize synthetic data for stress testing, fraud detection models, and risk assessment without the risk of exposing real customer data or violating privacy laws.

• Autonomous vehicle training

The development of autonomous vehicles relies heavily on synthetic data for training algorithms in object recognition, decision-making, and scenario simulation, providing a safe and efficient way to cover diverse driving conditions and scenarios.

• Retail and e-commerce

Synthetic data helps in customer behavior analysis, product recommendations, and market research, allowing retailers to enhance customer experience and optimize inventory management without relying on sensitive customer data.

• Cybersecurity

In cybersecurity, synthetic data is used to simulate network traffic, attack patterns, and other security threats to test and improve security systems and protocols.

• Augmented Reality (AR) and Virtual Reality (VR)

Synthetic data plays a crucial role in developing and training AR and VR systems, providing realistic simulations for training, entertainment, and educational purposes.

Tools 

Synthetic data generation tools can range from open-source libraries designed for research and development to commercial platforms offering advanced features for enterprise use.
Here we have hand-picked a set of tools commonly used for synthetic data generation.

• Synthea: An open-source simulator that generates synthetic patient data for healthcare applications. Synthea models the medical history of synthetic patients, producing realistic but not real patient data for research and analysis without compromising privacy.
• DataRobot: Offers AI and data science solutions, including synthetic data capabilities. It's designed for businesses looking to build predictive models without risking sensitive data exposure.

• Hazy: A commercial tool that generates synthetic datasets that statistically resemble your original data, allowing for safe sharing and analysis while protecting sensitive information.

• MOSTLY AI: A tool specialized in generating synthetic tabular data, ideal for customer data privacy and compliance. It uses AI to create data that is structurally similar to the original dataset but doesn’t contain any real user information.

• Mockaroo: A flexible tool for developers and testers to create realistic datasets up to 1,000 rows for free with SQL and JSON download options. It's ideal for smaller projects or initial testing phases.

• Tonic: Targets businesses needing to create synthetic versions of their databases for development, testing, or analytics purposes. Tonic offers fine-grained control over the data anonymization and synthesis process, ensuring that the synthetic data remains useful for its intended purpose while eliminating personal information.

• Artbreeder: Focused on creating synthetic images, Artbreeder allows users to blend and modify images using GANs, making it useful for creating varied visual content without real-world limitations.

Advantages of synthetic data

Synthetic data offers numerous benefits across various domains, from improving data privacy to enhancing machine learning model training. Here are some key advantages:

1. Privacy and security

Synthetic data enables the use of realistic data sets without exposing real-world sensitive information, thus maintaining privacy and complying with data protection regulations like GDPR.

2. Data accessibility 

It provides access to data where real data might be scarce, expensive to collect, or subject to ethical concerns, particularly in fields like healthcare, finance, and social research.

3. Bias reduction

By carefully generating synthetic data, it's possible to mitigate biases present in real-world datasets, leading to fairer and more equitable AI and machine learning models.

4. Improved model training

Synthetic data can be used to train machine learning models where real data is insufficient, especially for rare events or scenarios, enhancing the models' robustness and accuracy.

5. Testing and validation

It allows for the thorough testing of systems, software, and algorithms in a controlled but realistic environment, identifying potential issues before deployment with real-world data.

6. Cost efficiency

Generating synthetic data can be more cost-effective than collecting and cleaning real-world data, especially when considering the costs associated with data privacy compliance and the potential risks of data breaches.

7. Customizability

It offers the flexibility to create datasets with specific characteristics or conditions that might be difficult to capture with real data, allowing for targeted research and development efforts.

8. Ethical use of data

It addresses ethical concerns associated with using real data, especially in sensitive fields, by providing an alternative that doesn't involve real individuals' data.

Challenges and limitations

While synthetic data presents numerous advantages, it also comes with its own set of challenges and limitations that must be carefully navigated. These include:

1. Accuracy and realism

One of the primary challenges is ensuring that synthetic data accurately reflects the complexity and nuances of real-world data. There's always a risk that synthetic datasets may oversimplify or fail to capture critical patterns and anomalies present in the original data, potentially leading to misleading analysis or model training outcomes.

2. Bias and representation

Although synthetic data can help reduce bias, the algorithms used to generate it can inadvertently introduce new biases or perpetuate existing ones if not properly monitored and adjusted. Ensuring that synthetic data is representative of diverse populations and scenarios is crucial but challenging.

3. Ethical and legal considerations

The generation and use of synthetic data, especially when derived from real individuals' data, raise ethical questions regarding consent, privacy, and the potential misuse of synthetic data. Additionally, the legal landscape around synthetic data is still evolving, with uncertainties about data rights and responsibilities.

4. Complexity and resource requirements

Creating high-quality synthetic data often requires sophisticated algorithms and significant computational resources. The development and maintenance of these systems can be complex and costly, potentially limiting access for smaller organizations or individual researchers.

5. Verification and validation

Verifying that synthetic data is a valid substitute for real data involves comprehensive testing and validation. This process can be time-consuming and requires expertise to ensure that the synthetic data maintains the integrity of the original data's statistical properties.

6. Generalization

There's a risk that models trained on synthetic data may not perform well on real data due to overfitting or underestimation of real-world variability. Ensuring that synthetic data leads to models that generalize well to new, unseen data is a significant challenge.

7. Data dependency

The quality of synthetic data is heavily dependent on the quality of the input data or the assumptions made during its generation. Poor quality or inaccurate input data can lead to synthetic data that is misleading or of limited utility.

8. Public perception and trust

There may be skepticism or lack of trust in synthetic data and the models trained on it, especially in critical applications like healthcare and public policy. Building trust in synthetic data's reliability and ethical use is essential but can be challenging.

The role of synthetic data for machine learning

Synthetic data plays a transformative role in machine learning by providing a versatile solution to challenges such as data scarcity, privacy concerns, and biased datasets. By artificially generating data that mimics real-world phenomena, machine learning models can be trained, tested, and validated across diverse scenarios without relying on hard-to-obtain datasets. 

This approach not only facilitates the development of more robust and generalizable models but also ensures compliance with stringent data privacy regulations.

Synthetic data enables the exploration of edge cases and rare events, enhancing model performance and reliability in real-world applications, thus accelerating innovation and pushing the boundaries of what machine learning can achieve.

Synthetic data examples and use cases

With its various applications, synthetic data helps solve business problems at scale. In this section, we have provided examples of 3 use cases of synthetic data for machine learning. 

Case 1: Facial recognition systems for airport security

Airports need highly accurate facial recognition systems for security screenings, but collecting diverse facial images across various ethnicities, lighting conditions, and angles while ensuring privacy is challenging.

Synthetic data solution: Using Generative Adversarial Networks (GANs) to create a vast dataset of synthetic faces, incorporating a wide range of ethnicities, ages, facial expressions, and occlusions (e.g., glasses, hats).
Each synthetic face is generated to mimic real-world variations in lighting and background, ensuring the facial recognition system can accurately identify individuals in diverse airport environments.

Results: The enhanced facial recognition system, trained on this comprehensive synthetic dataset, shows significantly improved accuracy and reduced bias in real-world tests, leading to faster, more secure airport screenings without compromising passenger privacy.

Case 2. Financial fraud detection scenarios for banks

Financial institutions struggle to detect fraud due to the rarity and ever-evolving nature of fraudulent transactions, coupled with the need to protect customer data privacy.

Synthetic data solution: Develop a simulation model that generates synthetic banking transactions, including a variety of fraud scenarios (e.g., identity theft, unusual large transfers) along with normal transactions. This model uses historical fraud patterns to create realistic, but not real, transaction data, enabling the training of machine learning models on detecting subtle signs of fraud.

Results: Machine learning models trained on this synthetic dataset become adept at identifying fraudulent transactions with high precision, reducing false positives and enhancing the bank's ability to protect customer accounts, all while maintaining compliance with data privacy regulations.

Case 3. Healthcare diagnostics with synthetic medical imaging

Medical research and diagnostics training require large datasets of medical images, such as X-rays or MRI scans, which are limited due to patient privacy concerns and the rarity of certain conditions.

Synthetic data solution: Implement a combination of deep learning techniques to generate synthetic medical images showcasing a wide range of conditions, including rare diseases, with variations in patient demographics. These images are annotated with accurate diagnostic information for training purposes.

Results: Diagnostic models trained on this synthetic dataset show improved accuracy in detecting and diagnosing a wide range of conditions, including those not frequently observed in available real patient datasets. This leads to better patient outcomes through earlier and more accurate diagnoses.