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#homomorphic-encryption News & Analysis

9 articles tagged with #homomorphic-encryption. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

9 articles
DeFiBullishNewsBTC · Jun 187/10
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Zama And Morpho Push Confidential DeFi With New USDC Yield Vault

Zama and Morpho have launched a USDC yield vault leveraging fully homomorphic encryption (FHE) on Ethereum, advancing the confidential DeFi space by enabling yield farming with encrypted transactions. This development combines privacy-preserving computation with decentralized finance, addressing growing demand for confidential on-chain activities while maintaining composability with existing DeFi protocols.

Zama And Morpho Push Confidential DeFi With New USDC Yield Vault
$ETH
AI × CryptoBullisharXiv – CS AI · Jun 107/10
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Post-Quantum Secure Federated DeFi for Inclusive Banking

Researchers propose a post-quantum secure federated DeFi framework that combines lattice-based cryptography with homomorphic encryption to enable collaborative lending between banks while protecting against future quantum computing threats. The system uses encrypted data processing and geospatial AI models to assess creditworthiness of underserved borrowers, tested on agricultural lending in rural Virginia.

AI × CryptoBullisharXiv – CS AI · Mar 56/10
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Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI

Researchers introduce ZKFL-PQ, a quantum-resistant cryptographic protocol for federated learning in medical AI that combines zero-knowledge proofs, lattice-based encryption, and homomorphic encryption. The protocol achieves 100% rejection of malicious updates while maintaining model accuracy, addressing vulnerabilities from gradient inversion attacks and future quantum threats.

AI × CryptoBullishHugging Face Blog · Aug 27/106
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Towards Encrypted Large Language Models with FHE

The article discusses the development of encrypted large language models using Fully Homomorphic Encryption (FHE) technology. This approach would allow AI models to process data while keeping it encrypted, potentially addressing privacy concerns in AI applications.

AINeutralarXiv – CS AI · May 276/10
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Practical Anonymous Two-Party Gradient Boosting Decision Tree

Researchers introduce an anonymous gradient-boosted decision tree (GBDT) protocol enabling secure training on vertically partitioned data between two parties while hiding record identifiers. The approach uses dual circuit-PSI and oblivious pseudorandom functions to eliminate ID exposure risks inherent in standard private set intersection methods, while achieving computational efficiency comparable to non-private approaches.

AIBullishBankless · Feb 276/107
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Small Models Could Crack the Private AI Problem

Small AI models are emerging as a potential solution for private AI applications while fully homomorphic encryption remains years away from frontier-scale deployment. The threshold for what constitutes 'good enough' privacy-preserving AI has been lowered, making smaller models more viable for practical use cases.

AI × CryptoBullishHugging Face Blog · Nov 176/107
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Sentiment Analysis on Encrypted Data with Homomorphic Encryption

The article discusses techniques for performing sentiment analysis on encrypted data using homomorphic encryption. This approach allows analysis of sensitive data while maintaining privacy, potentially enabling new applications in finance and other sectors requiring data confidentiality.

AI × CryptoNeutralVitalik Buterin Blog · Jul 201/102
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Exploring Fully Homomorphic Encryption

The article title suggests coverage of Fully Homomorphic Encryption (FHE), a cryptographic technique that allows computations on encrypted data without decryption. However, no article body content was provided for analysis.