Live PreviewGretel
Features
- Synthetic data generation — Generate high-quality artificial data that maintains statistical accuracy without revealing real data.
- Differential privacy — Gain insights from aggregate data while keeping individual data secure.
- Federated learning — Train machine learning models without directly accessing sensitive data, ensuring privacy.
- Data transformation in-browser — Perform data transformations privately, enabling accurate modeling without moving sensitive data.
- Automatic sensitive data classification — Streamline data discovery and governance by automatically detecting and classifying sensitive information.
- Preservation of relational integrity — Maintain functional accuracy across tables with synthetic data, ensuring relational databases remain intact.
Use Cases
- Securing PII/PHI Data — Gretel.ai allows healthcare professionals and researchers to generate synthetic health data for research, ensuring that sensitive personal information remains protected while still being useful for analysis.
- Anonymizing Databases — Data engineers can use Gretel.ai to anonymize production databases, preserving relational integrity and enabling analytics without compromising sensitive information.
- Augmenting Limited Datasets — Data scientists can boost the diversity of their training datasets with Gretel.ai, enhancing model performance while maintaining privacy and compliance with data protection regulations.
- Enabling Data Sharing — Business leaders can facilitate safer data sharing across teams and organizations, leveraging Gretel.ai’s privacy solutions to eliminate disclosure risks while collaborating on sensitive projects.
- Federated Learning — Organizations looking to train collaborative machine learning models without moving sensitive data can benefit from Gretel.ai’s federated learning capabilities, ensuring data privacy throughout the process.
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