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.

通过匿名化实现数据隐私和保护是数据拥有者在将数据转发至其他可能用途之前需要解决的关键问题。本文提出了使用 CTGAN 生成的合成数据作为真实数据的替代品，以解决 OpenWiFi 网络中泄露和信息丢失的问题，并通过聚类算法的相似性评估以及无监督聚类验证指标的比较，表明 K-means 算法在合成数据与真实数据的相似性评估上表现出色。因此，实验结果展示了使用 CTGAN 生成的合成数据作为匿名化数据替代品的可行性。