Machine learning made secure / fast / efficient
Preserving Data Privacy
With machine learning, gaining insights from private or protected data comes down to choosing which compromises are most livable. Choose from (1) unbreakable privacy protection, (2) fast analysis, or (3) a simple, inexpensive approach. It’s impossible to get all three. It may be possible to get two out of three. But that’s still a long-shot…until we created the GEM. The GEM is a high-fidelity data abstraction that captures the analytical value of data, while completely preserving privacy. The core of our platform, it enables fast and efficient machine learning while offering unbreakable privacy protection when analyzing secure data.
Our technology ensures that data remains encrypted end-to-end while still allowing multiple parties to collaborate on model training or data analysis without transferring or exposing raw information. By integrating federated learning’s decentralized approach, Gemsen’s system allows for secure data sharing across entities, maintaining privacy across distributed sources while avoiding the latency issues typically associated with encryption.
This solution opens new possibilities for real-time, privacy-preserving machine learning across applications where data sensitivity and performance demands are both exceptionally high. With Gemsen’s technology, organizations no longer need to choose between speed, security, or scalability—they can have it all.