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

Data Protection in Big Data Analysis

"Big data" applications are collecting data from various aspects of our lives more and more every day. This fast transition has surpassed the development pace of data protection techniques and has resulted in innumerable data breaches and privacy violations. To prevent that, it is important to ensure the data is protected while at rest, in transit, in use, as well as during computation or dispersal. We investigate data protection issues in big data analysis in this thesis. We address a security or privacy concern in each phase of the data science pipeline. These phases are: i) data cleaning and preparation, ii) data management, iii) data modelling and analysis, and iv) data dissemination and visualization. In each of our contributions, we either address an existing problem and propose a resolving design (Chapters 2 and 4), or evaluate a current solution for a problem and analyze whether it meets the expected security/privacy goal (Chapters 3 and 5). Starting with privacy in data preparation, we investigate providing privacy in query analysis leveraging differential privacy techniques. We consider contextual outlier analysis and identify challenging queries that require releasing direct information about members of the dataset. We define a new sampling mechanism that allows releasing this information in a differentially private manner. Our second contribution is in the data modelling and analysis phase. We investigate the effect of data properties and application requirements on the successful implementation of privacy techniques. We in particular investigate the effects of data correlation on data protection guarantees of differential privacy. Our third contribution in this thesis is in the data management phase. The problem is to efficiently protecting the data that is outsourced to a database management system (DBMS) provider while still allowing join operation. We provide an encryption method to minimize the leakage and to guarantee confidentiality for the data efficiently. Our last contribution is in the data dissemination phase. We inspect the ownership/contract protection for the prediction models trained on the data. We evaluate the backdoor-based watermarking in deep neural networks which is an important and recent line of the work in model ownership/contract protection