DPNeT: Differentially Private Network Traffic Synthesis with Generative Adversarial Networks
DPNeT: Differentially Private Network Traffic Synthesis with Generative Adversarial Networks
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DOI：
10.1007/978-3-030-81242-3_1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Liyue Fan;Akarsh Pokkunuru
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文献类型：
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作者：
Liyue Fan;Akarsh Pokkunuru
高质量的网络流量数据可以共享，以实现知识发现和推进网络防御研究。然而，由于其敏感性，确保此类数据的安全共享一直是一个具有挑战性的问题。当前用于共享网络数据的方法存在几个限制来平衡隐私（例如，信息泄漏）和效用（例如，可用性和有用性）。为了克服这些限制，我们开发DPNeT，网络流量合成解决方案，产生高质量的网络流量，并满足（，）-差分隐私。我们采用生成对抗网络（GANs）来捕获真实的网络流的特征，并为混合类型属性提供了一个保持相似性的嵌入模型。此外，我们提出了新的技术，以改善差分私人学习的结果，并提供整体解决方案的隐私分析。通过对大规模网络流数据的综合评估，我们证明了我们的解决方案能够产生真实的网络流。
High quality network traffic data can be shared to enable knowledge discovery and advance cyber defense research. However, due to its sensitive nature, ensuring safe sharing of such data has always been a challenging problem. Current approaches for sharing networking data present several limitations to balance privacy (e.g., information leakage) and utility (e.g., availability and usefulness). To overcome those limitations, we develop DPNeT, a network traffic synthesis solution that generates high-quality network flows and satisfies (,)-differential privacy. We adopt generative adversarial networks (GANs) to capture the characteristics of real network flows and a similarity-preserving embedding model for mixed-type attributes. Furthermore, we propose new techniques to improve the outcome of differentially private learning and provide the privacy analysis of the overall solution. Through a comprehensive evaluation with large-scale network flow data, we demonstrate that our solution is capable of producing realistic network flows.