Definition of Verifiable Computing
Verifiable computing enables a client to outsource computational tasks to an untrusted server while receiving a succinct proof that the computation was executed correctly. In AI research, this is implemented as zkML (Zero-Knowledge Machine Learning).
Core Technologies
- ZK-SNARKs: Cryptographic primitives that allow for the verification of high-parameter model execution without access to model weights or input data.
- Fully Homomorphic Encryption (FHE): A method allowing computations to be performed on ciphertext, maintaining data privacy during inference.
- TEE (Trusted Execution Environments): Hardware-based security (e.g., Intel SGX, ARM TrustZone) that provides an isolated enclave for sensitive AI computation.
Foundational & Alternative Approaches
- Proof-Carrying Code (PCC): A 1990s technique where an executable contains a formal proof of its safety and correctness, verifiable by the host.
- Interactive Proof Systems: The theoretical precursor to ZKPs, defining how a prover can convince a verifier of a statement's truth.
- Optimistic Verification: A protocol where computations are assumed correct unless challenged, utilizing "fraud proofs" to maintain integrity in decentralized AI networks.
- Watermarking AI Models: Research into embedding hidden signals in model outputs to prove provenance and prevent unauthorized redistribution.
Analysis
The application of zkML provides a mechanism for the formal auditing of autonomous systems. It allows for cryptographic guarantees that an agent has adhered to its specifications and evidence protocols without compromising proprietary datasets.
References
- ezkl (GitHub): A framework for generating ZK proofs for models in the ONNX format.
- Modulus Labs (GitHub): Research and tooling for zero-knowledge proofs in AI inference.