What is Secret Computing®?
Secret Computing® enables data scientists to compliantly, securely, and privately compute on distributed data without ever exposing or moving it.
Don’t pick between data privacy and data usability — you can finally have both!
Why leverage Secret Computing®?
More data = better predictions.
Data scientists agree on this universal equation.
IT’s SIMPLE: SECURELY Access more data for better predictions, ALL while respecting data privacy.
Secret Computing® enables data scientists to unlock sensitive data for their machine learning and analytical models while meeting their organization’s privacy, security, and compliance requirements.
Some of the world’s largest financial services, technology, and manufacturing companies leverage Secret Computing® products to:
+ Securely share data-driven insights
+ Build privacy-compliant machine learning models on distributed data sources
+ Migrate to zero-knowledge cloud computing
+ Monetize insights without giving away the data
+ Facilitate secure data sharing in inter-organizational partnerships
How does Secret Computing® actually work?
Short answer: Encryption in-use technology. (It’s not magic. It’s cryptography.)
Long answer: Historically, encryption has taken one of two forms: encryption at-rest and encryption in-transit. Encryption at-rest means encrypting data that does not move. Encryption in-transit means encrypting data that moves through a network. These two encryption methods are largely effective, but fail to address one major vulnerability: encrypting data while it is being processed.
Inpher’s Secret Computing® products leverage advanced cryptographic principles that keep data encrypted while it is being processed.
Breakthroughs in encryption in-use technologies addresses this remaining data vulnerability and serve as the foundation for Inpher’s Secret Computing® technology. Specifically, Secret Computing® encompasses two complementary encryption in-use techniques:
Secure Multiparty Computation (MPC): a cryptographic protocol that distributes a computation across multiple parties where no individual party can see the other parties’ data
Fully Homomorphic Encryption (FHE): an encryption scheme that enables analytical functions to be run directly on encrypted data while yielding the same encrypted results as if the functions were run on plaintext