# NetDPSyn: Synthesizing Network Traces under Differential Privacy

arXiv: https://arxiv.org/abs/2409.05249 · HTML: https://mlsanity.com/paper/2409.05249/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2024.

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

- [DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis](https://mlsanity.com/paper/2503.14681/) (2025)
- [PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning](https://mlsanity.com/paper/2512.07342/) (2025)
- [Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data](https://mlsanity.com/paper/2602.02766/) (2026)

## Abstract (arXiv)

As the utilization of network traces for the network measurement research becomes increasingly prevalent, concerns regarding privacy leakage from network traces have garnered the public's attention. To safeguard network traces, researchers have proposed the trace synthesis that retains the essential properties of the raw data. However, previous works also show that synthesis traces with generative models are vulnerable under linkage attacks. This paper introduces NetDPSyn, the first system to synthesize high-fidelity network traces under privacy guarantees. NetDPSyn is built with the Differential Privacy (DP) framework as its core, which is significantly different from prior works that apply DP when training the generative model. The experiments conducted on three flow and two packet datasets indicate that NetDPSyn achieves much better data utility in downstream tasks like anomaly detection. NetDPSyn is also 2.5 times faster than the other methods on average in data synthesis.