A Multi-Level Consistent and Efficiently Running Privacy Protection Framework for Serverless Computing

Serverless computing's cost-efficiency and reliability drive its adoption in heterogeneous network data distribution, yet challenges persist in data leakage risks and multi-level consistency maintenance. Current privacy-preserving approaches for multilevel network structure consistency suffer from insufficient coupling of heterogeneous nodes to edge weights, unreasonable privacy budget allocation for differential privacy preservation of edge weights, and insufficient consideration of node sensitivity in maintaining multi-level structure consistency. Therefore, we propose a multi-level consistency efficient operational privacy protection framework for serverless computing. First, a hierarchical multi-type node similarity measure is designed to ensure the consistency of node features and edge weights. Second, develop a privacy budget allocation method based on the feature values of subgraph structures to avoid the waste of privacy budget. Third, introduce a spectral clustering method based on differential privacy combined with a graph reconstruction strategy to protect the privacy of community subgraphs and ensure community structural consistency during privacy perturbation. Finally, inter-community privacy protection is achieved by edge node diffusion and isomorphic substitution in a serverless runtime environment. Experimental evaluations on three real datasets of different sizes show that our framework not only effectively balances data privacy and availability, but also achieves efficient multi-level consistency maintenance in a serverless environment.