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Data privacy and Privacy regulations limit how much data a company can access for analysis

HIPAA, GDPR and Data Residency requirements restrict your ability to conduct data science. CryptoNumerics removes these restrictions.

CryptoNumerics enables you to create privacy protected datasets with quantifiable privacy risk. In addition, you can build statistical and machine learning models that protect people’s privacy.

HIPAA, GDPR and Data Residency requirements restrict your ability to conduct data science. CryptoNumerics removes these restrictions.

CryptoNumerics enables you to create privacy protected datasets with quantifiable privacy risk. In addition, you can build statistical and machine learning models that protect people’s privacy.

Use Cases

Financial Services

One of the largest aggregators of credit card transaction data wanted to significantly improve data utility of its assets, so it  could help its business partners such as retailers, banks, and ad companies achieve better conversion on ad campaigns, improve customer satisfaction etc. All these, while complying with privacy regulations and safeguarding consumer information.

With CryptoNumerics they were able to derive the same analytical value before privacy protection was applied. Thus opening new partnership opportunities that were restricted due to privacy issues.

Life Sciences

A large healthcare network, with millions of patients, wanted to unlock patient data for research, training of new staff and improving patient outcomes.  Additionally, they needed to comply with privacy regulations.

With CryptoNumerics they were able to preserve the granularity of the relevant analytical data elements while protecting all the identifying attributes, ensuring compliance. 

Privacy Crash Course

Six Things to Look for in Privacy Protection Software

This is the fourth blog in our Crash course in Privacy series   Enterprises want to: Leverage their data assets Comply with privacy regulations Reduce the risk exposure of consumer information. If the goal is to maintain data utility while protecting privacy here...

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Why Masking and Tokenization Are Not Enough

This is the third blog in our Crash course in Privacy series   Protecting consumer privacy is much more complex than just removing personally identifiable information(PII). Other types of information such as quasi-identifiers can re-identify individuals or expose...

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Why Protecting Sensitive Data is Important

This is the second blog in our Crash course in Privacy series   Privacy risk is the probability of extracting information about a specific individual in a dataset. Organizations must protect the significant personal information they have from exposure....

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