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

Rise of big data – issues and challenges

The recent rapid rise in the availability of big data due to Internet-based technologies such as social media platforms and mobile devices has left many market leaders unprepared for handling very large, random and high velocity data. Conventionally, technologies are initially developed and tested in labs and appear to the public through media such as press releases and advertisements. These technologies are then adopted by the general public. In the case of big data technology, fast development and ready acceptance of big data by the user community has left little time to be scrutinized by the academic community. Although many books and electronic media articles are published by professionals and authors for their work on big data, there is still a lack of fundamental work in academic literature. Through survey methods, this paper discusses challenges in different aspects of big data, such as data sources, content format, data staging, data processing, and prevalent data stores. Issues and challenges related to big data, specifically privacy attacks and counter-techniques such as k-anonymity, t-closeness, l-diversity and differential privacy are discussed. Tools and techniques adopted by various organizations to store different types of big data are also highlighted. This study identifies different research areas to address such as a lack of anonymization techniques for unstructured big data, data traffic pattern determination for developing scalable data storage solutions and controlling mechanisms for high velocity data

Quantification of De-anonymization Risks in Social Networks

The risks of publishing privacy-sensitive data have received considerable attention recently. Several de-anonymization attacks have been proposed to re-identify individuals even if data anonymization techniques were applied. However, there is no theoretical quantification for relating the data utility that is preserved by the anonymization techniques and the data vulnerability against de-anonymization attacks. In this paper, we theoretically analyze the de-anonymization attacks and provide conditions on the utility of the anonymized data (denoted by anonymized utility) to achieve successful de-anonymization. To the best of our knowledge, this is the first work on quantifying the relationships between anonymized utility and de-anonymization capability. Unlike previous work, our quantification analysis requires no assumptions about the graph model, thus providing a general theoretical guide for developing practical de-anonymization/anonymization techniques. Furthermore, we evaluate state-of-the-art de-anonymization attacks on a real-world Facebook dataset to show the limitations of previous work. By comparing these experimental results and the theoretically achievable de-anonymization capability derived in our analysis, we further demonstrate the ineffectiveness of previous de-anonymization attacks and the potential of more powerful de-anonymization attacks in the future.Comment: Published in International Conference on Information Systems Security and Privacy, 201

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone's life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone's personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can't apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by  Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.Â

Preserving Link Privacy in Social Network Based Systems

A growing body of research leverages social network based trust relationships to improve the functionality of the system. However, these systems expose users' trust relationships, which is considered sensitive information in today's society, to an adversary. In this work, we make the following contributions. First, we propose an algorithm that perturbs the structure of a social graph in order to provide link privacy, at the cost of slight reduction in the utility of the social graph. Second we define general metrics for characterizing the utility and privacy of perturbed graphs. Third, we evaluate the utility and privacy of our proposed algorithm using real world social graphs. Finally, we demonstrate the applicability of our perturbation algorithm on a broad range of secure systems, including Sybil defenses and secure routing.Comment: 16 pages, 15 figure

An Analytical Review of Privacy Preservation Using K Anonymity along with Bayesian Classifier in Data Mining

Privacy Preservation for social media is one of most trending research subject around the world. In addition to its trendiness, it is a very sensitive issue also. People around the world share their private information on the social media without thinking that it may affect their privacy. In such a condition it becomes the unrest duty to prevent the user information which is private. A lot of research workers have already put their ideas on table for the same issue. This paper studies the effect of Bayesian network in contrast to the prevention of the private data over social media. This paper also describes the pros and cons of using Bayesian Network for privacy preservation and also it compares some of the ethical prevention algorithms for the same. The evaluation has been done on the basis of ethical data mining parameters like Precision, Recall, F-Measur

Investigation on Security Issues and Features in Social Media Sites (Face Book, Twitter, & Google+)

Social media sites allow users to communicate and share their information which are a matter of privacy for users, so users should be aware about its limitations and disad-vantages to use social media sites. Likewise, there are many social media sites with its dif-ferent features and it typically works with the latest technology that is provided by the ex-perts to get connected and go along with the flow. The online privacy issues have been a re-al time problem and these however is the main aim for the experts to reduce the problems while sharing the kind of content that is allowed by the social media sites. There are issues that are general and the public need to oppose for the privacy terms and conditions. People these days are concerned about the information that they post on the sites such as Face Book, Twitter and Google Sharing the photos and the latest features are now a sign of a problem for many users. In this research paper, Researcher will explore key infor-mation about the privacy issues and problems reported by social media users while using social networking sites. Being a personal user of popular social networking sites, researcher faced privacy concerns that initiated me to conduct a research on actual facts and figures behind the privacy issues among the social networking sites. Nowadays, social media sites are widely used by hackers and un-authorized users where over usage of social media users from different geographic locations lead to in-creased privacy issues across these sites. In order to resolve the privacy concerns, the social media administrators have implemented many secured anti-privacy attack technique tech-niques but still they are not totally successfully providing 100% security to the user privacy over social networking sites. Researcher found this issue to be a serious concern in current cyber crimes for which we have decided to conduct a research on this topic. Researcher had conducted research on pri-vacy issues and found that there are loads of security concerns in terms of privacy issues such as user privacy while photos and videos upload user privacy during messaging and chatting, user privacy during shares and uploads. All these issues seemed to be critical in terms of security where there is a huge necessity for implementing effective security tech-niques that are highly capable in reducing privacy issues and ensure 100% privacy to the social media users. With use of this study researcher will explain the key privacy concerns reported in social media sites and current approaches available to reduce those issues. Researcher will explain the current scope of using those techniques and their limitations in providing privacy to the users. Finally, with use of this paper, researcher will investigate and propose best security technique that can be implemented to reduce the privacy concerns across social media sites

A comparison of clustering and modification based graph anonymization methods with constraints

In this paper a comparison is performed on two of the key methods for graph anonymization and their behavior is evaluated when constraints are incorporated into the anonymization process. The two methods tested are node clustering and node modification and are applied to online social network (OSN) graph datasets. The constraints implement user defined utility requirements for the community structure of the graph and major hub nodes. The methods are benchmarked using three real OSN datasets and different levels of k?anonymity. The results show that the constraints reduce the information loss while incurring an acceptable disclosure risk. Overall, it is found that the modification method with constraints gives the best results for information loss and risk of disclosure.This research is partially supported by the Spanish MEC (projects ARES CONSOLIDER INGENIO 2010 CSD2007-00004 -- eAEGIS TSI2007-65406-C03-02 -- and HIPERGRAPH TIN2009-14560-C03-01)Peer Reviewe