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.

TRUSTED BY GLOBAL INNOVATORS

  • “Working with Inpher to leverage their technology to access non-public data sources aligns with our objective to enhance our informational edge and generate value for the CPP Fund over the long run.”

    Daniel Wroblewski
    Daniel WroblewskiManaging Director, Alpha Gen Lab, CPP Investments
  • “We are excited to be partnering on a proposition that will deliver a secure, end-to-end consented data sharing solution leveraging Inpher’s patented technology, enterprise-ready platform and expert team for the next wave of innovation.”

    Danny Tyrrell
    Danny TyrrellCofounder, DataCo Technologies
  • “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

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