Enterprises struggle to leverage sensitive data for AI and machine learning workflows due to stringent data privacy regulations like GDPR, CCPA and HIPAA. Sharing data across borders and with external organizations introduces further compliance complexities and risks of re-identification.

tomtA provides a privacy-enhancing technology that transforms sensitive data into a statistically equivalent, anonymized replica, enabling organizations to unlock the full potential of their data for AI development and data sharing while maintaining compliance with data privacy laws. The platform employs computational differential privacy and advanced AI techniques to analyze and model intricate patterns and relationships within the original datasets. tomtA's algorithms optimize for robust privacy protection and exceptional precision, generating a data replica that preserves granular details and multidimensional correlations. This approach ensures that complex interdependencies between all data attributes are accurately captured, empowering users to leverage a fully anonymized representation of their sensitive information for a wide array of data-driven applications.

AI-powered algorithm built on computational differential privacy for generating safe and accurate data.

Preservation of granular details and multidimensional correlations within the data.

Generation of statistically equivalent data that maintains the utility of the original dataset.

Provides privacy, accuracy, and utility metrics to validate and trust the generated anonymized data, including epsilon loss bound, attribute and membership disclosure risk.

Preservation of rare instances within the data.

The primary target audience includes MLOps teams, data scientists, and AI/ML engineers in industries such as life sciences, fintech, energy, manufacturing, transportation, and logistics who require safe and accurate data for AI development and data sharing.