Access to broader and deeper data pools drives business value through better insights. Deliver secure, private and compliant AI applications with Secret Computing®.




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More Data Drives Better Business Decisions.

Data silos within and across organizations can block machine learning and analytics initiatives. Accelerate time to market and unlock the value of data in a secure, private and compliant manner by enabling privacy-preserving machine learning and analytics with Inpher’s enterprise-ready XOR Secret Computing® Product Suite.

Privacy Preserving Machine Learning Enables Secure Data Collaboration

Collaborate on private data across departments, jurisdictions and organizations without exposing or transferring sensitive information. Your data is always encrypted and never revealed.

Privacy Preserving Machine Learning Improves Business Insight Accuracy

Deliver high model accuracy and precision with encryption-in-use rather than existing privacy approaches that reduce predictive features or inject noise.

Privacy Preserving Machine Learning Help you Exceed Regulatory Compliance Requirements

Turn sensitive data to business value through encrypted computations while exceeding the compliance needs of global privacy and data regulations such as GDPR.


  • “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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