# Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

arXiv: https://arxiv.org/abs/1909.13403 · HTML: https://mlsanity.com/paper/1909.13403/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions](https://mlsanity.com/paper/2101.09113/) (2021)
- [Time-series Generation by Contrastive Imitation](https://mlsanity.com/paper/2311.01388/) (2023)
- [Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis](https://mlsanity.com/paper/2412.16083/) (2024)
- [Making Logic a First-Class Citizen in Generative ML for Networking](https://mlsanity.com/paper/2506.23964/) (2025)
- [TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare](https://mlsanity.com/paper/2602.07033/) (2026)
- [IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception](https://mlsanity.com/paper/2305.00925/) (2023)
- [Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments](https://mlsanity.com/paper/2406.04890/) (2024)
- [Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study](https://mlsanity.com/paper/2412.16207/) (2024)
- [Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation](https://mlsanity.com/paper/2512.00638/) (2025)
- [TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation](https://mlsanity.com/paper/2601.15663/) (2026)
- [Synthetic Time Series Generation via Complex Networks](https://mlsanity.com/paper/2601.22879/) (2026)
- [Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters](https://mlsanity.com/paper/2605.06032/) (2026)

## Abstract (arXiv)

Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge. As a specific target, our focus in this paper is on time series datasets with metadata (e.g., packet loss rate measurements with corresponding ISPs). We identify key challenges of existing GAN approaches for such workloads with respect to fidelity (e.g., long-term dependencies, complex multidimensional relationships, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity). To improve fidelity, we design a custom workflow called DoppelGANger (DG) and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DG achieves up to 43% better fidelity than baseline models. Although we do not resolve the privacy problem in this work, we identify fundamental challenges with both classical notions of privacy and recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges. By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.