artificial intelligence
Data owners frequently hesitate to share data with Machine Learning developers due to concerns about leaks, theft, illegal use, and protecting trade secrets
By 2025
80%
of the largest global organizations will have participated at least once in Federated Machine Learning to create more accurate, secure, and environmentally sustainable models.
60%
of large organizations will use Privacy-Enhancing Computation techniques to protect privacy in untrusted environments or for analytics purposes.
ensuring that data remains accessible only to its owner during ML models training
ideal for those working with Machine Learning and Sensitive Data
protecting data at all stages of ML development, from model training
to algorithm application
compatible with a wide range of popular Data Types and ML Architectures
Secure transmission of ML model parameters
No data storage
Our product handles it all — you don’t need to be a Python guru, FL expert, cryptographer, data scientist, or sysadmin
Obtaining the network's response in a secure manner
Secure Inference
Meet internal security and compliance standards to prevent project interruptions
Benefit
Enhance ML model quality
without raw data transmission
Benefit
Monetize knowledge safely,
not datasets
Benefit
Stand out by ensuring
data confidentiality
Benefit
Comply with data
security regulations
Federated Learning
Building a Collective Brain from Scattered Thoughts
FL allows multiple parties to contribute information to train a machine learning model, all while keeping their original data private.
It's like a collaborative brainstorming session where everyone contributes ideas without revealing their thought process.
Homomorphic Encryption
Private Computation on Model Parameters — No Decryption Needed
Think of it as solving a puzzle without ever taking it out of the box. With HE, model parameters stay encrypted even during training and inference.
No raw data is touched — and yet, everything works. This means ultra-secure collaboration with zero compromise on model quality.
Differential Privacy
Noise Where It Matters
By adding calibrated noise to model parameters, it prevents leakage of individual information.
The result? Robust privacy with models that stay sharp and effective.
