Privacy Preservation in Analytics
Our research shows that at least 60% of “anonymized” datasets have a high risk of re-identification
HIPAA, GDPR, and Data Residency requirements restrict your ability to conduct data science.
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.
Privacy Automation
Complying with privacy regulations requires a combined effort from many stakeholders inside an organization. Our Privacy Automation solutions simplify the compliance process by allowing privacy rules definition, risk assessments, application of privacy actions and compliance reporting to happen within a single application.
Virtual Data Collaboration
Organizations are constantly trying to augment their dataset, however, there are situations in which the additional data can’t be accessed due to privacy or IP concerns. Our Virtual Data Collaboration solution allows organizations to work with data partners and extract insight from combined datasets without ever exposing or moving the raw data.
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. To maintain data utility while protecting privacy, here is a list of...
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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...
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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 data set. Organizations must protect the significant personal information they have from exposure....
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Understanding the Difference Between Data Privacy and Data Security
This is the first blog in our Crash Course in Privacy series. Privacy is all over the news these days, from Facebook scandals to European fines associated with failing to comply with GDPR. This is caused, in part, because protecting the privacy of your...
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