Build better and more accurate models with secure, compliant access to sensitive data. Secret Computing® eliminates the tradeoff between data privacy and usability.




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More Data Helps Build Better Models.

Yet, much of it is often locked away from ML initiatives due to privacy and security risks. Secret Computing® empowers data teams to leverage sensitive data securely and build accurate models.

Train models with more features or more samples by leveraging sensitive data across multiple privacy zones – from within or across organizations – without guaranteeing localization and privacy-preservation.

Access data from your notebook and popular ML frameworks. Our Python library and REST API enable advanced cryptographic functions with minimal code changes.

Secure your models from various privacy attacks such as inference and reconstruction. Unlock multiple ML use cases like secure federated learning and encrypted model serving for compliant data processing.


  • “We are excited to support Inpher as they continue to pioneer Secret Computing®, further improving consumer and client confidence in data privacy and security controls.”

    Paul Bernard
    Paul BernardDirector of Amazon Alexa Fund
  • “Advances in data science and cryptography mean that we no longer have to accept the traditional tradeoff between security, privacy and usability when handling data of any kind. Inpher is at the vanguard of this revolution.”

    Samik Chandarana
    Samik ChandaranaHead of Data & Analytics J.P.Morgan Chase
  • “The ‘bad guys’ have all the same technologies we do. But the one thing they cannot obtain is the scale of training data we could through collaborative sharing, such as through the potential Inpher offers.”

    Hays W. “Skip” McCormick
    Hays W. “Skip” McCormickData Science Lead BNY Mellon


The Privacy Risk Right Under Our Nose in Federated Learning

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Inpher Wins iDASH’20 Secure Genome Competition

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