Bibliometric Survey of Privacy of Social Media Network Data Publishing

We are witness to see exponential growth of the social media network since the year 2002. Leading social media networking sites used by people are Twitter, Snapchats, Facebook, Google, and Instagram, etc. The latest global digital report (Chaffey and Ellis-Chadwick 2019) states that there exist more than 800 million current online social media users, and the number is still exploding day by day. Users share their day to day activities such as their photos and locations etc. on social media platforms. This information gets consumed by third party users, like marketing companies, researchers, and government firms. Depending upon the purpose, there is a possibility of misuse of the user\u27s personal & sensitive information. Users\u27 sensitive information breaches can further utilized for building a personal profile of individual users and also lead to the unlawful tracing of the individual user, which is a major privacy threat. Thus it is essential to first anonymize users\u27 information before sharing it with any of the third parties. Anonymization helps to prevent exposing sensitive information to the third party and avoids its misuse too. But anonymization leads to information loss, which indirectly affects the utility of data; hence, it is necessary to balance between data privacy and utility of data. This research paper presents a bibliometric analysis of social media privacy and provides the exact scope for future research. The research objective is to analyze different research parameters and get insights into privacy in Social Media Network (OSN). The research paper provides visualization of the big picture of research carried on the privacy of the social media network from the year 2010 to 2019 (covers the span of 19 years). Research data is taken from different online sources such as Google Scholar, Scopus, and Research-gate. Result analysis has been carried out using open source tools such as Gephi and GPS Visualizer. Maximum publications of privacy of the social media network are from articles and conferences affiliated to the Chinese Academy of Science, followed by the Massachusetts Institute of Technology. Social networking is a frequently used keyword by the researchers in the privacy of the online social media network. Major Contribution in this subject area is by the computer science research community, and the least research contribution is from art and science. This study will clearly give an understanding of contributions in the privacy of social media network by different organizations, types of contributions, more cited papers, Authors contributing more in this area, the number of patents in the area, and overall work done in the area of privacy of social media network

Privacy Preserving User Data Publication In Social Networks

Recent trends show that the popularity of Social Networks (SNs) has been increasing rapidly. From daily communication sites to online communities, an average person\u27s daily life has become dependent on these online networks. Additionally, the number of people using at least one of the social networks have increased drastically over the years. It is estimated that by the end of the year 2020, one-third of the world\u27s population will have social accounts. Hence, user privacy protection has gained wide acclaim in the research community. It has also become evident that protection should be provided to these networks from unwanted intruders. In this dissertation, we consider data privacy on online social networks at the network level and the user level. The network-level privacy helps us to prevent information leakage to third-party users like advertisers. To achieve such privacy, we propose various schemes that combine the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We combine the concepts of k-anonymity and l-diversity to achieve user privacy. To provide user-level privacy, we consider the scenario of mobile social networks as the user location privacy is the much-compromised problem. We provide a distributed solution where users in an area come together to achieve their desired privacy constraints. We also consider the mobility of the user and the network to provide much better results

All liaisons are dangerous when all your friends are known to us

Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user profiling in social networks is described, and its performance is reported and discussed. Secondly, the experiments --conducted on information usually considered sensitive-- reveal that by just publicizing one's contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined.Comment: 10 pages, 5 table

De-anonymyzing scale-free social networks by using spectrum partitioning method

Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anonymization into node matching problem in graph, and the de-anonymization method can reduce the number of nodes to be matched at each time. In addition, we use spectrum partitioning method to divide the social graph into disjoint subgraphs, and it can effectively be applied to large-scale social networks and executed in parallel by using multiple processors. Through the analysis of the influence of power-law distribution on de-anonymization, we synthetically consider the structural and personal information of users which made the feature information of the user more practical

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

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