01.
Candidate data generation
AI generates candidate data independently, without training on the original dataset.
The foundation of secure, privacy-safe data innovation
CUBIG’s Data Non-Access Technology and differential privacy work together to guarantee complete protection of original data — no exposure, no compromise, full control.
Contact us
Generate synthetic data safely — without touching the source
CUBIG’s AI creates candidate data which your original dataset then validates through a secure voting process. The original data remains untouched at every stage.
02.
Voting by original data
Secure voting allows the original dataset to select the best-fit synthetic candidate.
03.
Regeneration based on feedback
DTS refines each dataset iteratively, regenerating synthetic data until performance precision is achieved.
04.
Final secure synthetic data
The result is verified synthetic data — high-fidelity, compliant and fully privacy-protected.
Differential privacy that protects without performance loss
Applied at the generation stage, not during model training. This ensures privacy protection without degrading model performance.
Traditional differential privacy limitations
- #1.Reduced data utility
- #2.Model performance degradation
CUBIG’s differential privacy advantages
- #1.Applied during voting, never on the original model
- #2.Maintains model integrity and data quality
Performance comparison
CUBIG technology
99% fidelity
Conventional Approach
14%
Engineered for precision and privacy
01.
Accurate
High-performance synthetic data with no degradation of the original model. Zero risk, full reliability.
02.
Adaptable
Smart data selection ensures maximum usability across analytics, testing and development environments.
03.
Secure
Synthetic data customised for your needs through prompt-based automation — precise to your domain and dataset.
100% data protection — no exposure, no compromise
Experience next-generation data protection with CUBIG’s Data Non-Access Technology and differential privacy. Keep every dataset secure while generating high-quality synthetic data that performs like the real thing — without compromise.