ZK-based identity vs FHE-based identity

Proven scalability: ZK-SNARKs and STARKs enable verification in milliseconds on-chain, with projects like Polygon ID and zkPass handling millions of proofs. This matters for high-throughput applications like decentralized exchanges (DEXs) or gaming where speed is critical.

Established developer stack: Libraries like Circom, Halo2, and Noir, plus verifiers on every major chain (Ethereum, Starknet, zkSync). This matters for teams prioritizing integration speed and access to a large pool of experienced developers.

Cryptographic minimalism: Proves a specific claim (e.g., "I am over 18") without revealing the underlying data (your birthdate). This is the gold standard for regulatory compliance (KYC) and credential verification where data minimization is legally required.

Computation on encrypted data: Enables private credit scoring, encrypted voting, and confidential DAO governance where inputs and results remain hidden. This matters for complex, multi-party computations that ZK's 'prove, don't reveal' model can't handle.

Stateful confidentiality: Data (e.g., health records, financial history) can be stored and used repeatedly in its encrypted form. This matters for building long-term, private user profiles or recurring services like subscriptions or loyalty programs.

No proof generation overhead: Users don't need to generate a new ZK proof for every interaction. This reduces client-side computation and latency, which matters for mobile-first applications or services requiring real-time, private data feeds.

Proven verification speed: ZK-SNARKs (e.g., Circom, Halo2) enable sub-second proof verification on-chain. This matters for high-throughput applications like private voting (e.g., MACI) or selective credential disclosure where the verification cost is the primary bottleneck.

Established tooling and adoption: Frameworks like Circom, Noir (Aztec), and zk-SNARKs libraries have thousands of GitHub stars and active developer communities. Protocols like Worldcoin (Proof of Personhood) and Semaphore (anonymous signaling) are in production. This matters for teams needing battle-tested, auditable code.

Computation on encrypted data: Unlike ZK's prove-verify model, FHE (e.g., Zama's fhEVM, Fhenix) allows direct computation on encrypted identity states. This matters for private reputation scoring, real-time eligibility checks, or encrypted DAO governance where the state itself must remain private, not just the proof of its validity.

Enhanced trust minimization: Many FHE schemes (e.g., CKKS, BFV) are trustless by design, eliminating the need for a toxic waste ceremony required by some ZK-SNARKs. This matters for protocols where the credibility of a one-time setup is a political or security risk.

Static proof statements: ZK circuits must be compiled for specific logic (e.g., "I am over 18"). Any change requires a new circuit. This is a con for applications needing dynamic, ad-hoc queries on private data, like a privacy-preserving social graph search.

High computational cost: FHE operations are orders of magnitude slower than plaintext or ZK verification. A simple encrypted addition can be ~1Mx slower. This is a major con for consumer-facing dApps requiring low-latency responses or running on resource-constrained devices.

Proven, mature cryptography: ZK-SNARKs (used by zkSync, Starknet) and ZK-STARKs provide succinct, publicly verifiable proofs of identity claims without revealing underlying data. This is ideal for selective disclosure (e.g., proving age > 21 on Polygon ID) and reputation portability (e.g., Sismo badges).

Off-chain proof generation, on-chain verification: Computation is client-side, keeping on-chain costs predictable. Final verification is fast and cheap (e.g., < $0.01 on Ethereum L2s). This suits high-frequency, low-latency actions like DeFi access gating or Sybil-resistant airdrops.

Static proofs, limited computation: A ZK proof validates a specific, pre-defined statement. To update or compute on private data (e.g., "my credit score changed"), a new, costly proof must be generated from scratch. This hinders dynamic, stateful identity systems.

Computation on encrypted data: FHE (as implemented by Fhenix, Inco) allows direct operations on encrypted identity attributes. This enables private, stateful profiles where data can be updated and queried without ever decrypting it, crucial for ongoing creditworthiness checks or private voting systems.

Supports complex logic and aggregation: Protocols can compute functions (averages, thresholds) across multiple users' encrypted data. This unlocks private attestation pools and collective reputation scores without exposing individual inputs, a key differentiator for DAO governance and underwriting.

High computational overhead & nascent tooling: FHE operations are 100-1000x more computationally intensive than ZK, leading to higher gas costs and latency. The ecosystem (SDKs, prover networks) is less mature than ZK's (Circom, Halo2). This currently limits it to lower-throughput, high-value applications.