Machine Learning as a Service raises the joint challenge of protecting client inputs and model parameters while enabling public verification of inference results. Existing verifiable privacy-preserving approaches often incur costly online proof generation or provide limited support for nonlinear operations. We present AuditML, a publicly auditable privacy-preserving framework for machine learning inference in the semi-honest model. AuditML combines arithmetic secret sharing with commitment-based audit data and separates execution from verification: the parties generate correlated randomness offline, perform privacy-preserving inference online, and publish the opened values and associated audit data to a public bulletin board, from which anyone can verify the computation afterward. The supported inference circuits are expressed using three auditable operations, ADD, MULTIPLY, and SIGN. In particular, AuditML implements SIGN and its underlying comparison entirely over arithmetic shares, avoiding arithmetic-to-Boolean conversion while retaining commitment-based auditability. This design eliminates a separate online proof-generation procedure and keeps auditing outside the latency-critical online phase. We implement AuditML on MP-SPDZ and evaluate linear regression, fixed-threshold binary logistic classification, and linear-kernel SVM in two-party and three-party settings. At a batch size of 50, linear and fixed-threshold binary logistic inference complete online in less than one second, while the multiplication-intensive SVM requires 10.0939–40.0334 seconds; per-participant online communication is approximately twice that of the semi-honest baseline. And compared with zkCNN-MPL, AuditML has lower inference latency with the same level of security. The results demonstrate efficient online execution for the evaluated lightweight models while exposing the higher computation and audit costs of multiplication-intensive circuits.
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