Four network-traffic generators leak their training data: membership inference reaches 88% TPR and network identifiers can be fully recovered
The study introduces a privacy measurement suite for synthetic network traffic spanning membership inference, data extraction, and network-specific attacks on identifiers, attributes, and topology, and evaluates four representative generators (NetShare, NetDiffusion, TrafficLLM, NetSSM) across five datasets, finding that even with black-box-only attackers and minimal training, membership inference reaches up to 0.88 TPR at FPR≤0.01 and up to 100% of network identifiers can be recovered, while anonymization and DP noise each cover only part of the risk at a utility cost.