Leverage collaborative machine learning to improve model prediction and accuracy.
Enables organizations to access additional sensitive data to train machine learning models or run inference on decentralized data without revealing sensitive information, PII, or IP.
With Duality, organizations across industries can utilize a range of data science, statistical, and machine learning models while ensuring privacy throughout the analytics lifecycle. Companies can choose to work with encrypted data or encrypted models, run analytics or train and fine-tune models, and easily configure and control which data and models are used. This ensures that every party remains in control of their owned assets, regardless of the environment in which they are computing.
Support the most popular frameworks such as Linear, Logistic, XGboost, CNN and more.
Apply pre-processing script or transformation to prepare the data to perform model training, inference or any statistical computations.
Extensive set of secured statistical computations such as cross tabulation, anomaly detection, Kaplan-Meier, log rank and more.
Ensure full control on your data, decide who accesses the data, which computations will run, at what frequency, and more.
Link datasets based on a unique ID/PII or based on multiple quasi-ID/PII parameters (e.g., name, address, date of birth…).
Improve accuracy and matches rate through dedicated algorithm for fuzzy matching of Names, Addresses, or common algorithm like SOUNDEX.
Improve AI/ML model performance by training, tuning and validating AI and ML models on encrypted data.
Optimize efficiency by working collaboratively across borders while meeting data privacy and residency requirements.
Analyze sensitive medical data across centers to gain deeper insights to speed research and discovery.
Access new high-value data sets in a privacy-protected and compliant manner.
Protect sensitive data and models while transitioning AI workloads to the cloud.
Apply third-party analytics and ML models to your sensitive data without exposing it.
Deliver personalized customer experiences and upsell with partners while preserving privacy.
Create new revenue sources from sensitive data while preserving privacy and regulatory compliance.
Maximize the value of sensitive, regulated, or confidential data.
