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BC Platforms Enlists TripleBlind for AI Data Protection

NEW YORK – BC Platforms said Tuesday that has formed a partnership with enterprise data privacy company TripleBlind to build a privacy-preserving, federated layer of artificial intelligence for the firm's BCRquest.com global network of biobanks.

BC Platforms said that it needs the TripleBlind technology to safeguard data as part of its participation in Privacy Preserving AI for Synthetic and Anonymous Health Data (PRIVASA) consortium, a two-year project to promote access to and sharing of health information by Finnish companies. BC Platforms is headquartered in Zurich, Switzerland, but runs its research and development of Espoo, Finland.

The firm recently received €6.5 million ($7.8 million) from Business Finland, a government agency that promotes Finnish companies abroad, to support its participation in PRIVASA.

For the partnership, Kansas City, Missouri-based TripleBlind is supplying its proprietary encryption technology to BC Platforms for muiltiomic data analysis and results delivery within the framework of BCRquest.com, which supplies research cohorts for pharmaceutical R&D.

"In partnership with TripleBlind, our new federated AI learning platform, which is based upon data from BCRquest's global genomic and clinical database network, could significantly speed up research and development without compromising on patient privacy or [intellectual property rights]," BC Platforms Chief Security Officer and Founder Timo Kanninen said in a statement. Kanninen said that this also will help accelerate adoption of personalized medicine.

"We are excited to be partnering with BCP, coupling our next-generation private data-sharing technology with BCRquest's data network for use in research environments with strict regulatory standards," including in HIPAA in the US, the European Union's General Data Protection Regulation, and the California Consumer Privacy Act, TripleBlind CEO and Cofounder Riddhiman Das said. "By improving privacy, we will enable the use of richer, more diverse third-party clinical and genomic datasets, facilitating the development of more accurate and less biased novel AI-based models."

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