FHE, MPC and TEEs explained for blockchain and AI
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Short explainers of fully homomorphic encryption, multi-party computation and trusted execution environments, with use cases in private AI training, encrypted smart contracts, private voting and privacy-preserving DeFi.
FHE, MPC, TEEs.
Bullish, but WTF does it all mean?
Short explainers. ↓
➤ Fully Homomorphic Encryption (FHE)
FHE is a cryptographic technique that allows computations to be performed directly on encrypted data without decrypting it first.
This ensures data privacy throughout the entire computational process.
In blockchain and AI, FHE is pivotal because it enables private data analysis, decentralized AI model training, and secure computation across distributed systems (among many other use cases).
For instance, AI models can be trained on encrypted data from multiple sources without exposing sensitive information, which is essential in sectors like finance and healthcare.
FHE also bears great potential to enable encrypted smart contracts and transactions without revealing any of the underlying information.
➤ Multi-Party Computation (MPC)
MPC allows multiple parties to collaboratively compute a function over their private inputs without revealing the inputs to each other.
This is crucial for decentralized systems, where trust among parties is limited.
In blockchain, MPC can be used to enhance privacy in smart contracts, enabling private voting, secure auctions, and privacy-preserving DeFi applications.
When combined with FHE, MPC allows for even more secure and efficient computations in decentralized environments, supporting collaborative AI without compromising data privacy.
➤ Trusted Execution Environments (TEE)
TEEs are secure areas of a processor that ensure code and data loaded inside are protected with respect to confidentiality and integrity.
In blockchain and AI contexts, TEEs provide a layer of trust when running decentralized applications or performing computations that involve sensitive data.
They are particularly useful for ensuring that computations on encrypted data (like those enabled by FHE) are performed securely, even on untrusted hardware.
TEEs can be key for privacy-preserving AI inference and secure on-chain computation.
For more context on why these technologies matter and a list of projects you cant fade, check my post below!
DYOR. ↓
QUOTING @zerokn0wledge_
Privacy matters. 🥷 Even more so in the age of (personalized) AI, where fine-tuned AI/ML models will increasingly work with sensitive, high-value data and where AI agents will dominate on-chain activity. While advanced and potentially specialized AI models bear great potential t
Privacy matters. 🥷 Even more so in the age of (personalized) AI, where fine-tuned AI/ML models will increasingly work with sensitive, high-value data and where AI agents will dominate on-chain activity. While advanced and potentially specialized AI models bear great potential t