Barabási-Albert çizgesinde K-Derece anonimleştirmenin performans analizi

Anonymity is one the most important problems that emerged with the increasing number of graphbased social networks. It is not straightforward to ensure anonymity by adding or removing some nodes from the graph. Therefore, a more sophisticated approach is required. The consideration of the degree of the nodes in a graph may facilitate having knowledge about specific nodes. To handle this problem, one of the prominent solutions is k-degree anonymization where some nodes involving particular degree values are anonymized by masking its information from the attackers. Our objective is to evaluate the achievement of k-degree anonymization with a well-known graph structure, namely, Barabási-Albert graph, which is similar to the graphs on social networks. Hence, we generate multiple synthetic Barabási-Albert graphs and evaluate the k-degree anonymization performance on these graphs. According to experimental results, the success of k-degree anonymity approximately proportional to the number of edges or nodes.Anonimlik, çizge tabanlı sosyal ağların sayısının artmasıyla ortaya çıkan en önemli sorunlardan biridir. Çizgeye bazı düğümler ekleyerek veya çıkararak anonimliği sağlamak kolay değildir. Bu nedenle, daha komplike bir yaklaşım gereklidir. Çizgenin yapısı veya çizgedeki düğümlerin derecesi, belirli düğümler hakkında bilgi sahibi olmayı kolaylaştırabilir. Bu sorun için öne çıkan çözümlerden biri olan k-derece anonimleştirme, belirli dereceleri içeren bazı düğümlerin bilgilerinin saldırganlardan gizlenerek anonimleştirilmesidir. Amacımız, sosyal ağlardaki çizgelere benzeyen Barabási-Albert çizgesi gibi iyi bilinen bir çizge yapısı ile k-derece anonimleştirmenin başarısını değerlendirmektir. Bu nedenle, birden çok sentetik Barabási-Albert çizgesi değerlendiriyoruz. Deneysel sonuçlara göre, k-derece anonimliğin başarısı, yaklaşık olarak kenar veya düğüm sayısı ile orantılıdır

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure