InvMetrics: Measuring Privacy Risks for Split Model-Based Customer Behavior Analysis

Mobile Edge Computing (MEC) has great potential to facilitate cheap and fast customer behavior analysis (CBA). Model splitting, widely adopted in collaborative learning of MEC, partitions the CBA models between the customer device and the edge server in a layer-wise manner to support efficient distributed learning. However, the split-model architecture (SMA) is vulnerable to data reconstruction attacks for privacy leakage in intermediate data, of which the risk measurements remain unexplored. In this paper, we propose a privacy risk measurement framework, called InvMetrics, for split model-based CBA systems, which assess the degree of privacy leakage from both the CBA owners' and the regulators' perspectives. For CBA owners, we propose a privacy metric, Distance Loss (DLoss), based on distance correlation, which is computationally efficient, and thus eligible for being deployed on the customers' devices. For third-party evaluators, we propose Uncertainty Loss (ULoss) based on entropy, which can measure privacy risk without accessing raw data. Evaluation results on three CBA datasets and one image dataset demonstrate that InvMetrics framework with DLoss and ULoss can accurately measure privacy risk and is more efficient than the state-of-the-art.