In the data-centric business domain, organizations need massive volumes of realistic data to train their AI models and systems. That said, real-world data has its share of concerns related to privacy and compliance issues.
Synthetic data provides a feasible alternative, allowing companies to generate artificial data to train their AI models. What exactly is synthetic data? Simply put, synthetic data is a replica of real-world data created using machine learning models. Synthetic data is not "fake" as it can mimic real-world data patterns, characteristics, and dependencies.
Besides ensuring data privacy, synthetic data can address data-related challenges such as:
- Missing data (or data gaps)
- Lack of compliance with data privacy regulations
- Associated costs and time spent on collecting real-world data
- Limited access to varied datasets – thus improving data quality and versatility
Here’s how synthetic data is revolutionizing privacy.
How does synthetic data ensure privacy?
For AI-driven applications and models, synthetic data presents an efficient medium to ensure data privacy and compliance – along with building secure AI models. By replicating the statistical properties of real-world data, synthetic data generators effectively remove the risk of exposing sensitive data such as personally identifiable information (PII).
Here are a few ways in which synthetic data ensures privacy and compliance:
- Pseudonymization
In this process, synthetic data generators replace sensitive data – such as customer name and address – with an artificial identifier (or pseudonym). This method allows synthetic data for AI research and data analysis – without exposing the privacy. With this technique, synthetically-generated data can link to the original real dataset with an additional token or key.
- Anonymization
As the name suggests, this process makes the synthetic data completely anonymous, thus ensuring privacy. This process either removes or obscures sensitive data most likely to be exploited. Further, anonymized data is not linked to the original (or any other) dataset.
Additionally, enterprises generating synthetic data can take the following measures to protect data privacy:
- On-premises deployment
With on-premises deployment, organizations can improve the privacy and compliance factor of real-world data. While cloud-based data storage systems can safeguard data privacy, organizations can potentially expose sensitive data like PII by storing them in the cloud. They can also consider on-premise deployment when synthetic data has in-built dependencies on sensitive information.
- Monitoring privacy-related metrics
During the process of synthetic data generation, enterprises must also track privacy-related metrics such as:
- Leakage score – measures the percentage of rows in the synthetic dataset that are identical to the original dataset. A high leakage score can lead to data leakage when the original dataset contains vital information about the target.
- Proximity score – measures the distance between the original and synthetic dataset. A smaller score can pose a higher risk to privacy.
Next, let’s discuss why synthetic data is better for building secure and compliant AI models.
How synthetic data can build secure and compliant AI models
As compared to real-world data, synthetic data is more suitable for building secure and compliant AI models and systems. Here’s why:
- Access to diverse datasets
Synthetic data generators can access diverse datasets to improve the performance of AI-powered models. In some cases, this can reduce the level of AI “bias,” which can lead to error-prone results.
- High data volume
Enterprises find it both challenging and time-consuming to acquire real-world data – particularly in the case of sensitive data (protected by industry regulations). On the other hand, synthetic data is faster to generate – and is not governed by privacy-related regulations. Effectively, AI models trained on diverse datasets and massive data volumes can offer improved performance and compliance.
- Model validation
Synthetic data can play a crucial role in testing AI systems before deployment. Within a controlled environment, QA professionals can use synthetic data to test the model performance under various scenarios. Real-world data poses risks to security and data integrity in the production environment. Synthetic data allows safe and effective testing of AI models in any environment.
- Data augmentation
Real data can have missing or insufficient values – along with data gaps that impact AI model performance. By using AI-generated synthetic data, enterprises can augment the data to retain the characteristics and properties of the original dataset.
- Rare scenario simulation
Synthetic data can also simulate rare scenarios (such as self-driving cars or medical emergencies) for AI models. For such scenarios, real-world data is difficult to acquire for training AI models.
Conclusion
With the growing usage of AI-powered models for various business use cases, synthetic data is a crucial component that can deliver both data privacy and compliance.
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