The healthcare industry has rich sources of sensitive medical datasets which are vital for gaining significant insights. However, the use of this sensitive data requires navigating a minefield of private patient information as well as sharing data between independent healthcare entities, to create a statistically significant dataset. The biggest challenge that the healthcare industry has is determining how to address data privacy regulations such as HIPAA, GDPR and other emerging regulations around the world. This includes data residency controls as well as enable sharing of data in a secure and private fashion.
How CryptoNumerics Supports Healthcare Organizations
CryptoNumerics addresses the four key ‘How-To’ data concerns for all healthcare and medical organizations looking to overcome privacy-related challenges.
|How to enable legal use of medical data for Data Science||Demonstrate both organizational and technical controls as well as processes for risk assessment for identification, data anonymization and pseudonymization. This is done through a single enterprise class application to support HIPAA and legitimate interest processing.|
|How to deliver high-value data utility for Data Science and Analytics||Overcome the issues of traditional encrypting or hashing datasets prior to publishing for Data Science and Analytics that removes the analytical value of the data.|
|How to apply privacy controls whilst maintaining high value data utility for Data Science and Analytics||Leverage next generation privacy preserving protection approaches such as differential privacy to meet data compliance while still retaining the data’s value for Data Science.|
|How to collaborate and share data with third parties to build and train models||Train and build models for Data Science using Secure Multi-Party Computing (SMPC). SMPC allows data collaboration in a legal and ethical way without requiring the need to share data.|
Use Case: Enabling Multi-Party Research while satisfying HIPAA and not moving the data
CryptoNumerics helped provide a framework which was applied to multi-party research, thus reducing time to insights, freeing up capacity for legal, compliance, and analytical teams, while providing healthcare networks with a frictionless framework for data collaborations.
Use Case: Realizing the potential of healthcare data
CN-Protect enabled the healthcare network to create privacy-protected datasets that could then be shared with pharmaceutical companies, healthcare payers, research organizations, and internally with different groups.
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