Adversarial Machine Learning-Enabled Anonymization of OpenWiFi Data

Abstract

Data privacy and protection through anonymization is a critical issue for network operators or data owners before it is forwarded for other possible use of data. With the adoption of Artificial Intelligence (AI), data anonymization augments the likelihood of covering up necessary sensitive information; preventing data leakage and information loss. OpenWiFi networks are vulnerable to any adversary who is trying to gain access or knowledge on traffic regardless of the knowledge possessed by data owners. The odds for discovery of actual traffic information is addressed by applied conditional tabular generative adversarial network (CTGAN). CTGAN yields synthetic data; which disguises as actual data but fostering hidden acute information of actual data. In this paper, the similarity assessment of synthetic with actual data is showcased in terms of clustering algorithms followed by a comparison of performance for unsupervised cluster validation metrics. A well-known algorithm, K-means outperforms other algorithms in terms of similarity assessment of synthetic data over real data while achieving nearest scores 0.634, 23714.57, and 0.598 as Silhouette, Calinski and Harabasz and Davies Bouldin metric respectively. On exploiting a comparative analysis in validation scores among several algorithms, K-means forms the epitome of unsupervised clustering algorithms ensuring explicit usage of synthetic data at the same time a replacement for real data. Hence, the experimental results aim to show the viability of using CTGAN-generated synthetic data in lieu of publishing anonymized data to be utilized in various applications.

arXiv comment8 pages, 4 Figures, "Wireless World Research and Trends" Magazine. Initial version was presented in 47th Wireless World Research Forum

References in corpus (6)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Data Synthesis based on Generative Adversarial Networks
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• Synthesizing Tabular Data using Generative Adversarial Networks
• PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
• Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach