OpenFHE - Open Source Fully Homomorphic Encryption

222 posts

OpenFHE - Open Source Fully Homomorphic Encryption banner
OpenFHE - Open Source Fully Homomorphic Encryption

OpenFHE - Open Source Fully Homomorphic Encryption

@OpenFHE_org

The open source OpenFHE software project. A community of researchers and developers developing open source fully homomorphic encryption (#FHE)

Katılım Haziran 2024
189 Takip Edilen252 Takipçiler
OpenFHE - Open Source Fully Homomorphic Encryption
We would welcome any and all contributions, feedback, suggestions, additions or any related content. We want to highlight all of the great work being done with OpenFHE. Community building is hard but important work. We want to promote all of our users and contributors!
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Optalysys
Optalysys@Optalysys·
At @EthereumDenver this week? Let's catch up! Our team is at D/Infra today, why not set up a meeting? You can drop Nic Lawrence - Director of Business Development - an email at nic.lawrence@optalysys.com or message in the comments🗓️
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d/Infra Summit
d/Infra Summit@d_InfraSummit·
16/ What if web3 could be both secure & transparent? Dr. Kurt Rohloff (Co-Founder & CTO @DualityTech & @openfhe_org) explores the latest in homomorphic encryption: powering private transactions, secure ML, and decentralized analytics without exposing sensitive data.
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d/Infra Summit
d/Infra Summit@d_InfraSummit·
1/ ✨ d/Infra Summit Speaker Announcements ✨ Join us in Denver on Feb 27 for deep dives into Scaling & App Platforms, ZK, DePIN, Confidential Compute, Decentralized AI, and more. We'll be updating this thread with new speakers over the next few days! 🧵👇
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Antonio Grasso
Antonio Grasso@antgrasso·
Privacy-enhancing technologies like homomorphic encryption, differential privacy, and federated learning redefine how businesses manage data, proving that safeguarding individual privacy doesn't have to come at the cost of losing insights. Microblog @antgrasso #PETs #Privacy Privacy-enhancing technologies (PETs) are advanced tools that allow secure data processing while safeguarding personal identities. Homomorphic encryption enables computations on encrypted data without decryption, maintaining strict confidentiality. Differential privacy ensures dataset utility by adding controlled noise, preventing the exposure of individual data points. Federated learning decentralizes analysis by keeping sensitive data on local devices, reducing the risks of breaches. These methods balance privacy and usability, ensuring compliance with regulations like GDPR while empowering businesses to leverage data responsibly and ethically. #DataSecurity #EthicalAI #DifferentialPrivacy #HomomorphicEncryption #FederatedLearning #DataProtection
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Nesa
Nesa@nesaorg·
EE vs HE The Equivariant Encryption paradigm as compared to Homomorphic Encryption.
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