Privacy-Preserving Data Science

Accessing Siloed Data for Data Science

 For a long time, enterprises believed data needed to be centralized in order to become a data-driven business. In reality, this is not always possible. Access to data is limited by Risk & Compliance and the challenges of centralizing data in a data lake are the reality.  The same applies to accessing siloed and de-centralized data cross-divisionally or with external parties. It is essential for data science to have this access to show real business value.

CryptoNumerics’ software solutions leverage breakthrough technologies: ‘secure multiparty computation’, ‘secret shares cryptography’ and ‘advanced privacy automation’ in a single integrated software platform that enables organizations to:

  • Leverage siloed data without relocating or exposing the data
  • Overcome Risk & Compliance challenges
  • Broaden and extend access to additional data for data science
  • Access data faster which would otherwise be delayed by legal and contractual negotiations

Accessing Siloed Data for Data Science

For a long time, enterprises believed data needed to be centralized in order to become a data-driven business. In reality, this is not always possible. Access to data is limited by Risk & Compliance and the challenges of centralizing data in a data lake are the reality.  The same applies to accessing siloed and de-centralized data cross-divisionally or with external parties. It is essential for data science to have this access to show real business value.

CryptoNumerics’ software solutions leverage breakthrough technologies: ‘secure multiparty computation’, ‘secret shares cryptography’ and ‘advanced privacy automation’ in a single integrated software platform that enables organizations to:

  • Leverage siloed data without relocating or exposing the data
  • Overcome Risk & Compliance challenges
  • Broaden and extend access to additional data for data science
  • Access data faster which would otherwise be delayed by legal and contractual negotiations
    Managing Data Utility for Data Science

    Most enterprises now have an entire team of data scientists providing consumers with better products, more tailored experiences, and improved outcomes in terms of healthcare, reduced risk, or better financial forecasts. In order to achieve these outcomes, enterprises must use Data Science with consumer information, which contains private data. 

    Performing analytics on private data poses many risks for an enterprise. For example: 

    • If a data breach were to occur, not only would the enterprise be held liable, but they would also lose customers and their brand would be damaged.
    • There are regulations that enterprises must ensure they are adhering to or pay fines. 

    CryptoNumerics’ software helps enterprises to: 

    • Apply optimal privacy protection to consumer information as a way to mitigate risk while retaining analytical value
    • Satisfy privacy regulations and ensure the data contains the highest amount of information according to a utility metric. Utility metrics give a measure of the usefulness of the data and should be customized based on each use case 

    Building Models with Sensitive Data

    In today’s world, data privacy and artificial intelligence are at odds due to the massive amount of data required to train a model and the sensitivity surrounding that data. Certain studies require sensitive datasets such as medical records, financial statements, location history, and voice transcripts. While using these data assets improve the quality of the models, accessing them is a challenge due to data residency issues, regulatory concerns, and issues around the ethical use of data.   

    CryptoNumerics’ software allows organizations to:

    • Create a privacy-protected dataset that retains analytical value allowing datasets to be used for building statistical and machine learning models
    • Use cryptographic techniques that enable building models across distributed internal data and external-party data without having to expose, share, or moved any underlying data

    Building Models with Sensitive Data

    In today’s world, data privacy and artificial intelligence are at odds due to the massive amount of data required to train a model and the sensitivity surrounding that data. Certain studies require sensitive data sets such as medical records, financial statements, location history, and voice transcripts. While using these data assets improve the quality of the models, accessing them is a challenge due to data residency issues, regulatory concerns, and issues around the ethical use of data.   

    CryptoNumerics’ software allows organizations to:

    • Create a privacy-protected dataset that retains analytical value allowing datasets to be used for building statistical and machine learning models
    • Use cryptographic techniques that enable building models across distributed internal data and external-party data without having to expose, share, or moved any underlying data
    Multi-Party Data Collaboration

    The demand for high-quality data along with the creation of statistically significant datasets is a high priority for data scientists and researchers. Accessing the data needed to accomplish these two tasks requires navigating through a minefield of sensitive data due to data residency issues, regulatory concerns, and ethical usage issues. This is a cumbersome process that adds additional costs to the Data Science process. 

     CryptoNumerics’ software simplifies the data access process by:

    • Streamlining and automating the application of privacy techniques
    • Providing a secure and private way to do collaborative analytics, exposing or relocating the data without having to expose, share, or moved any underlying data

     

      Analytics while Protecting IP

      Data-driven organizations understand that data is the raw material for powerful insights that drive desirable business outcomes, such as through improved product offerings, more relevant market development, and personalized customer experiences. 

      By combining your organization’s data with complementary datasets from external partners, you could provide even more potent input to drive your insights from. But how can you collaborate with data partners in a way that keeps your intellectual property protected, beyond just relying simply on contractual agreements or sending your data to third parties?   

      CryptoNumerics’ software solves this issue by:

      • Allowing you to build machine learning or statistical models with data partners without any of the parties disclosing or moving their data
      • Ensuring your data intellectual property is protected and within your firewalls
      • Enabling your data scientists to use our software platforms to build powerful models across datasets as if they were all in one location
      • Giving each party control over what types of models are built as well as how frequently

      Analytics while Protecting IP

      Data-driven organizations understand that data is the raw material for powerful insights that drive desirable business outcomes, such as through improved product offerings, more relevant market development, and personalized customer experiences. 

      By combining your organization’s data with complementary datasets from external partners, you could provide even more potent input to drive your insights from. But how can you collaborate with data partners in a way that keeps your intellectual property protected, beyond just relying simply on contractual agreements or sending your data to third parties?   

      CryptoNumerics’ software solves this issue by:

      • Allowing you to build machine learning or statistical models with data partners without any of the parties disclosing or moving their data
      • Ensuring your data intellectual property is protected and within your firewalls
      • Enabling your data scientists to use our software platforms to build powerful models across datasets as if they were all in one location
      • Giving each party control over what types of models are built as well as how frequently

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