zkML

zkML enables a prover to execute inference using a proprietary model and generate a cryptographic proof that the output is correct without revealing the model weights or architecture. The verifier learns only that a valid model produced the result.

• Protects intellectual property of proprietary models
• Enables model marketplaces where buyers verify quality without accessing weights
• Compatible with ZK-SNARKs and ZK-STARKs for proof generation

Users can submit sensitive data for inference and receive results without exposing the raw input to the model operator. The proof confirms correct computation occurred on the original, unmodified input while keeping that input encrypted or locally held.

• Enables medical diagnosis without revealing patient records
• Supports financial risk scoring with private transaction data
• Often combined with Fully Homomorphic Encryption (FHE) or Trusted Execution Environments (TEEs) for end-to-end privacy

Any third party can verify that a specific model was executed correctly on a specific input to produce a claimed output. This succinct proof is exponentially smaller than re-running the computation and can be verified in milliseconds.

• Eliminates trust in centralized inference providers
• Critical for decentralized oracle networks like Chainlink
• Enables audit trails for high-stakes AI decisions in regulated industries

Multiple inference proofs can be recursively composed into a single constant-size proof using recursive proof composition. This enables batching thousands of model executions into one verifiable attestation, dramatically reducing on-chain verification costs.

• Core technique used in zkVMs like RISC Zero
• Enables scalable verifiable AI pipelines
• Reduces gas costs for on-chain verification by orders of magnitude

Neural networks must be compiled into arithmetic circuits for proof generation. Post-training quantization reduces weight precision to integers, dramatically shrinking circuit size and proof generation time while preserving accuracy.

• INT8 and INT4 quantization reduce constraints by 10-100x
• Specialized compilers like Circom and Noir handle circuit generation
• Active research area balancing proof speed against model fidelity

zkML enables decentralized exchanges where model developers sell inference access without exposing weights, and buyers verify output correctness cryptographically. Smart contracts release payment only upon valid proof submission.

• Eliminates counterparty risk between model owners and consumers
• Enables pay-per-inference business models with cryptographic settlement
• Integrates with ZK-Rollups and Validium for scalable deployment

A Zero-Knowledge Succinct Non-Interactive Argument of Knowledge is the most widely deployed proof system for zkML applications. SNARKs produce constant-size proofs (often just a few hundred bytes) that can be verified in milliseconds, regardless of the complexity of the ML model. This succinctness makes them ideal for on-chain verification. However, SNARKs require a trusted setup ceremony to generate a common reference string. If the ceremony is compromised, false proofs could be generated. Popular implementations include Groth16 and Plonk.

A Zero-Knowledge Scalable Transparent Argument of Knowledge offers an alternative to SNARKs with distinct security properties. STARKs rely on collision-resistant hash functions rather than elliptic curve pairings, eliminating the need for a trusted setup and providing post-quantum security. They excel at proving large computations with faster prover times, making them suitable for complex deep learning models. The trade-off is larger proof sizes (typically tens to hundreds of kilobytes) compared to SNARKs. The FRI protocol is the core polynomial commitment scheme underlying STARKs.

A technique where a zero-knowledge proof attests to the validity of one or more previous proofs, enabling proof aggregation and compression. In zkML, this allows multiple inference proofs to be bundled into a single constant-size proof. For example, a zk-Rollup processing thousands of ML predictions can generate one compact proof for on-chain verification. Recursive composition also enables incremental verifiable computation, where long-running ML training pipelines can be proven step-by-step without the verifier needing to replay the entire process.