PrivGuardInfer: Channel-Level End-Edge Collaborative Inference Strategy Protecting Original Inputs and Sensitive Attributes
Yunhao Yao, Zhiqiang Wang, Puhan Luo, Yihang Cheng, Jiahui Hou, Xiang‐Yang Li · IEEE Transactions on Mobile Computing · 2025
End-edge collaborative inference improves computational efficiency by dividing a deep neural network into two parts, executed across the end device and the edge node in parallel. However, adversaries like malicious edge nodes can exploit transmitted data to reconstruct original inputs or infer sensitive attributes. Existing collaborative inference strategies upload the majority of input features to the edge node, significantly increasing the risk of privacy leakage, even without input reconstruction. Therefore, we propose PrivGuardInfer, a channel-level DNN end-edge collaborative inference strategy that optimizes intra-layer partition to simultaneously protect original inputs and sensitive attributes while ensuring latency constraints, supported by three key designs. First, the privacy measurements oriented both layer depth and channel count, jointly quantify the difficulty of reconstructing original inputs using varying numbers of feature maps across different layers. After assessing each channel's contribution, the information offset further measures the difficulty of inferring sensitive attributes. Finally, PrivGuardInfer models the privacy-optimal intra-layer partition under latency constraints as a grouped knapsack problem, mapping attack difficulty to item values and inference latency to item weights. Experimental results reveal that PrivGuardInfer achieves an average improvement of 80.54% in defending against model inversion attacks and 63.34% against attribute inference attacks compared to existing end-edge partition strategies. Moreover, it outperforms current privacy protection methods by an average of 69.37% and 49.75% in mitigating these two types of attacks.