Different PETs to the Rescue
High level on the different tech solutions.
Different Privacy Enhancing Technologies can help solve on-chain privacy, let us present a high level below:
Zero-Knowledge Proofs (ZK)
Idea: Prove that a computation is correct without revealing its inputs.
Pros:
High verification efficiency — great for on-chain proofs.
Strong cryptographic soundness (no need to trust hardware).
Ideal for proving statements like “this transfer is valid” without revealing amounts.
Cons:
Limited expressiveness (complex circuits are costly).
Expensive proving time and large setup requirements.
Harder to implement arbitrary private smart contracts.
Not suitable for private shared state (application like sealed bid auctions, election, and many more).
Secret Sharing–Based MPC
Idea: Split data into shares and distribute them among parties that jointly compute a function without revealing their shares.
Pros:
No single point of trust — privacy is distributed.
Scales well for recurring computations between fixed parties.
Cons:
High communication rounds overhead.
Requires multiple online parties during computation.
Poor performance for dynamic or public blockchain environments.
FHE-Based MPC (Fully Homomorphic Encryption)
Idea: Perform computation directly on encrypted data without ever decrypting it.
Pros:
Data never leaves encrypted form.
No need for multiple parties during computation (but needed for decryption).
Suitable for cloud or off-chain confidential processing.
Cons:
Extremely slow (105–108× slower than plaintext computation). Hardware acceleration may reduce this overhead, but still very slow.
Complex key generation and management.
Difficult to integrate with composable smart contracts.
Garbled-Circuit-Based MPC
Idea: Encode a computation as a Boolean circuit that can be securely evaluated by one or more parties using encrypted “wire labels.”
Pros:
Constant-round protocols with low latency.
Efficient for arbitrary logic and branching (smart contracts).
Very flexible, supporting computation of normal logic (e.g., ADD256) and cryptographic logic (e.g., AES, SHA functions).
Suitable for decentralized execution (as in gcEVM).
Cons:
Requires relatively high bandwidth to deliver the garbled circuits to the evaluator, e.g., about 1KB per Private ERC20 (but this does not impact the real-time performance - resources consumption to execute an ingress private workload is very low).
Trustless setup requires cryptographic coordination between parties.
Indistinguishable Obfuscation (iO)
Idea: Make code publicly available but cryptographically obfuscated so that its logic cannot be reverse-engineered.
Pros:
Theoretically maximal privacy — hides how computation works.
Could enable private shared state in public contracts.
Cons:
Still largely theoretical — inefficient far from practical today.
Relies on unproven or heavy cryptographic assumptions.
Hard to update or debug obfuscated logic.
Trusted Execution Environments (TEE)
Idea: Execute code inside secure hardware enclaves that isolate computation from the host OS.
Pros:
Fast and practical today (near-native performance).
Enables off-chain confidential compute with on-chain attestation.
Cons:
Requires trusting hardware vendors (e.g., Intel, ARM), which repeatedly break
Vulnerable to side-channel and firmware attacks.
Limited scalability and decentralization - poses many single points of failure.
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