Collaborate securely on sensitive assets without sacrificing privacy. Leverage any data type and any model to achieve the insights you need—from basic analytics to advanced AI model training. Unlock computation on data that was previously inaccessible.
By using secure federated AI, Duality allows organizations to access sensitive data and train machine learning models without revealing sensitive information, PII, or model IP.
Many organizations have models that need to be customized to clients, but the sensitivity of their data and that of the model itself prove to be major obstacles. Duality helps organizations create and scale new revenue streams with a workflow that protects both model IP and input data.
Our solutions allow you to control access, specify the computations performed, determine the operational frequency of the data, and more. Remove the trust from collaborative machine learning model training, tuning, and customization.
The Duality Federated AI Engine combines Federated Learning with advanced Privacy-Enhancing Technologies (PETs) and robust governance features. Teams can now analyze siloed datasets without moving them, focusing on delivering value instead of managing security concerns.
Leverage the NVIDIA FLARE platform alongside Confidential Computing (Trusted Execution Environments or TEEs), Differential Privacy, Privacy Rules, and a robust governance system—all seamlessly integrated into our secure data collaboration platform.
Duality utilizes FLARE, the leading open-source library for federated learning, empowering data teams and data owners with secure collaboration tools for sensitive data.
With Duality, AI and ML teams across industries can leverage a wide range of data science, statistical, and machine learning models while ensuring strong protection for both models and input data. This enables teams to streamline data acquisition, build higher-quality models, and deliver greater value by safeguarding sensitive client data.
Support any model and any data type (e.g. Linear, Logistic, XGboost, CNN , LLM etc..)
Apply pre-processing script or transformation to prepare the data to perform model training, inference or any statistical computations.
Meets the requirements of the EU AI Act in both the requirements of an AI Regulatory Sandbox and in simplifying the means for AI providers to acquire real-world data with which they must prove model efficacy and compliance.
Ensure full control and governance of input data, decide who accesses the data, which computations can run, at which frequency, and more while providing continuous integration and delivery of updates to your ML infrastructure.
Like most models, there are often multiple datasets that will be used for training. Easily link datasets based on a unique ID/PII or based on multiple quasi-ID/PII parameters (e.g., name, address, date of birth…).
When training on 3rd party data, it’s common to need to move the data to the model, or the model to the data. Secure Collaborative AI enables models to be run wherever the data resides while protecting both the model IP and input data.
Improve generative ML workflow and model performance by training, tuning, and validating ML models on encrypted data.
Optimize efficiency by working collaboratively across borders and accessing new high-value data sets while meeting data privacy and residency requirements.
Analyze sensitive medical data across centers to gain deeper insights to speed research and discovery.
Protect sensitive data and models while transitioning ML workloads to the cloud.
Apply third-party analytics and ML models to your sensitive data without exposing it to deliver personalized user experiences while preserving privacy.
Proving value to clients is critical for sales teams to effectively drive adoption. Scale model monetization efforts with a workflow that allows clients to input their data for model customization with guarantees of data protection and governance.
Empower your organizations to share and analyze encrypted data using Duality’s Privacy Enhancing Technologies (PETs) such as Fully Homomorphic Encryption, Federated Learning, Trusted Execution Environment, and more…
Enable privacy-preserving data collaborations across your entire financial ecosystem.
Protect patient data across your healthcare network through privacy-preserving collaborations.
Transform your marketing strategies with data collaborations that respect privacy, driving digital advertising efforts with confidence and creativity.
