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CyberWire Daily

Keeping data confidential with fully homomorphic encryption. [Research Saturday]

CyberWire Daily

N2K Networks, Inc.

News, Daily News, Tech News, Technology

4.81.1K Ratings

🗓️ 13 March 2021

⏱️ 23 minutes

🧾️ Download transcript

Summary

Guest Dr. Rosario Cammarota from Intel Labs joins us to discuss confidential computing. Confidential computing provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing their data or machine learning algorithms to the other party. This technique goes by several names — multiparty computing, federated learning and privacy-preserving analytics, among them. Confidential computing can enable this type of collaboration while preserving privacy and regulatory compliance. The research and supporting documents can be found here: Intel Labs Day 2020: Confidential Computing Confidential Computing Presentation Slides Demo video Learn more about your ad choices. Visit megaphone.fm/adchoices

Transcript

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0:00.0

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Search your first call to discover how SRM can help your business. Hello everyone and welcome to the CyberWire's research Saturday.

1:25.6

I'm Dave Bitner and this is our weekly conversation with researchers and analysts

1:30.4

tracking down threats and vulnerabilities, solving some of the hard problems of

1:35.0

protecting ourselves in a rapidly evolving cyberspace.

1:39.2

Thanks for joining us. So fully homomorphic encryption is an encryption technique, but unlike the type of encryption that we use right now, homomorphic encryption allows to keep

1:57.1

confidentiality of data while data is being in use.

2:01.6

That's Dr Rosario Camarota.

2:04.0

He's a principal engineer at Intel Labs.

2:07.1

The research we're discussing today is titled Confidential Computing, Advances in Federated learning and fully homomorphic encryption. You can sense to glory.

2:25.0

If this is started something.

2:27.0

Feel it in your bone.

...

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