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From a security-engineering point of view, @zama upgrades the baseline assumption about what an attacker sees.
On current public chains, we assume adversaries have a perfect transcript of every balance, parameter and user action in plaintext. FHEVM changes the model: adversaries still observe control flow and final outputs, but critical intermediate values are mathematically hidden.
For example, a liquidation engine can examine encrypted health factors and still trigger positions correctly, without revealing each user’s exact thresholds to the world.
This is more than “obfuscation”; it is a formal restriction on information leakage. When we include this in threat modelling, many previously “inevitable” attacks, like state-based MEV strategies, cease to be inevitable at all.
As an educator and researcher, this is precisely why I treat Zama’s FHEVM and its fully homomorphic encryption stack as a foundational building block for privacy-preserving onchain systems rather than just another temporary market narrative.
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