Abstract
Vertical Federated Learning (VFL) is an emerging distributed learning paradigm for cross-silo collaboration without accessing participants' data. However, existing VFL work lacks a mechanism to audit the inference correctness of the data party. The malicious data party can modify the local data and model to mislead the joint inference results. To exploit this vulnerability, we design a novel Vertical Federated Inference Tampering (VeFIT) attack, allowing the data party to covertly tamper with the local inference and mislead results on the task party's final prediction. VeFIT can decrease the task party's inference accuracy by an average of 34.49%. Existing defense mechanisms can not effectively detect this attack, and the detection performance is near random guessing. To mitigate the attack, we further design a Vertical Federated Inference Auditing (VeFIA) framework. VeFIA helps the task party to audit whether the data party's inferences are executed as expected during large-scale onli