Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic
This addresses privacy concerns for network security practitioners by providing actionable guidance to minimize leakage in synthetic traffic, though it is incremental as it builds on existing attack methods.
The paper tackled the problem of privacy leakage in synthetic network traffic generated by models, introducing comprehensive metrics to measure vulnerabilities, and found that privacy risks vary widely with membership inference attack success ranging from 0% to 88% and up to 100% of network identifiers recoverable.
To address the scarcity and privacy concerns of network traffic data, various generative models have been developed to produce synthetic traffic. However, synthetic traffic is not inherently privacy-preserving, and the extent to which it leaks sensitive information, and how to measure such leakage, remain largely unexplored. This challenge is further compounded by the diversity of model architectures, which shape how traffic is represented and synthesized. We introduce a comprehensive set of privacy metrics for synthetic network traffic, combining standard approaches like membership inference attacks (MIA) and data extraction attacks with network-specific identifiers and attributes. Using these metrics, we systematically evaluate the vulnerability of different representative generative models and examine the factors that influence attack success. Our results reveal substantial variability in privacy risks across models and datasets. MIA success ranges from 0% to 88%, and up to 100% of network identifiers can be recovered from generated traffic, highlighting serious privacy vulnerabilities. We further identify key factors that significantly affect attack outcomes, including training data diversity and how well the generative model fits the training data. These findings provide actionable guidance for designing and deploying generative models that minimize privacy leakage, establishing a foundation for safer synthetic network traffic generation.