This paper introduces novel cryptographic protocols for verifying the integrity of machine learning inference performed on untrusted edge computing nodes. We develop lightweight proof systems that enable clients to verify that edge servers correctly executed ML models without revealing sensitive model parameters or input data.
| TECHNICAL FOCUS | Edge Computing, Verifiable Computation, ML Security, Cryptographic Proofs |
| RESEARCH CATEGORY | Artificial Intelligence |
| PRIMARY OUTPUT |