# Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic

arXiv: https://arxiv.org/abs/2511.20497 · HTML: https://mlsanity.com/paper/2511.20497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 42 of its 98 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 30 of 42)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Scalable Extraction of Training Data from (Production) Language Models](https://mlsanity.com/paper/2311.17035/) (2023)
- [Analyzing Leakage of Personally Identifiable Information in Language Models](https://mlsanity.com/paper/2302.00539/) (2023)
- [Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://mlsanity.com/paper/2305.18462/) (2023)
- [The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks](https://mlsanity.com/paper/1911.07135/) (2019)
- [Do Membership Inference Attacks Work on Large Language Models?](https://mlsanity.com/paper/2402.07841/) (2024)
- [Are Diffusion Models Vulnerable to Membership Inference Attacks?](https://mlsanity.com/paper/2302.01316/) (2023)
- [Reconstruction and Membership Inference Attacks against Generative Models](https://mlsanity.com/paper/1906.03006/) (2019)
- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Membership Inference Attacks against Diffusion Models](https://mlsanity.com/paper/2302.03262/) (2023)
- [Membership Inference Attacks against Synthetic Data through Overfitting Detection](https://mlsanity.com/paper/2302.12580/) (2023)
- [Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models](https://mlsanity.com/paper/2310.15007/) (2023)
- [Membership Inference Attack Susceptibility of Clinical Language Models](https://mlsanity.com/paper/2104.08305/) (2021)
- [A Unified Framework for Quantifying Privacy Risk in Synthetic Data](https://mlsanity.com/paper/2211.10459/) (2022)
- [Membership Inference of Diffusion Models](https://mlsanity.com/paper/2301.09956/) (2023)
- [Black-box Membership Inference Attacks against Fine-tuned Diffusion Models](https://mlsanity.com/paper/2312.08207/) (2023)
- [This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models](https://mlsanity.com/paper/2101.05084/) (2020)
- [Property Inference Attacks Against GANs](https://mlsanity.com/paper/2111.07608/) (2021)
- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation](https://mlsanity.com/paper/2310.08543/) (2023)
- [New Directions in Automated Traffic Analysis](https://mlsanity.com/paper/2008.02695/) (2020)
- [NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging](https://mlsanity.com/paper/2310.04429/) (2023)
- [Semantic Membership Inference Attack against Large Language Models](https://mlsanity.com/paper/2406.10218/) (2024)
- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [Privacy Attacks on Image AutoRegressive Models](https://mlsanity.com/paper/2502.02514/) (2025)
- [TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation](https://mlsanity.com/paper/2504.04222/) (2025)

## Built on by (most cited first, 3 of 3)

- [TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data](https://mlsanity.com/paper/2607.03926/) (2026)
- [PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation](https://mlsanity.com/paper/2602.08331/) (2026)
- [MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining](https://mlsanity.com/paper/2605.11034/) (2026)

## Abstract (arXiv)

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.