Online Social Network Graphs Research Articles

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Privacy Preserving in Online Social Networks Using Fuzzy Rewiring

Privacy concerns of users threaten the usage of online social networks (OSN). In this regard, privacy preserving of OSN emerged as a convincing solution for preserving the privacy of users and uncovering useful insights from the social network data. In this article, we propose a novel algorithm based on the fuzzy sets and rewiring algorithm for preserving the privacy of users. This article presents the algorithm called privacy-preserving rewiring algorithm (PPRA), which can be used for anonymizing the social network data. The algorithm is validated by showing its effectiveness on four real-world datasets across three major graph mining tasks. The proposed PPRA algorithm will help in preserving the privacy of users in the OSN graph while simultaneously maintaining the utility that can be generated from the OSN graph structure.

Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.

Differential Private Social Network Publication and Persistent Homology Preservation

Online social networks (OSNs) service providers share and analyze users’ data for better performance. Hence, they are responsible for the protection of sensitive information. Previous researchers proposed several anonymization techniques for OSN data. Some anonymization techniques based on differential privacy claim to preserve privacy and graph utility under certain graph metrics. However, these graph utility metrics can partially describe the whole graph. In this paper, we employ persistent homology to have a comprehensive description of the OSN graph utility. The proposed scheme novelly preserves the persistent structures and differential privacy. In the proposed scheme, we employ the adjacency matrix model as the graph abstraction model. To strengthen privacy protection, we add exponential noise to the adjacency matrix. We then find the number of adding/deleting edges under the guidance of differential privacy. To preserve persistent homology, we collect edges along with the persistent structures. The proposed scheme carefully perturbs the edges while preserving those structures. Two anonymization sub-schemes, PHDP and rPHDP, have been proposed to balance the computation complexity and utility preservation. The evaluation results show that the proposed scheme outperforms other traditional anonymization schemes under graph utility metrics and graph application metrics.

Publishing Social Network Graph Eigenspectrum With Privacy Guarantees

Online social networks (OSNs) often refuse to publish their social network graphs due to privacy concerns. Recently, differential privacy has become the widely accepted criteria for privacy preserving data publishing. Although some work has been done on publishing matrices with differential privacy, they are computationally unpractical as they are not designed to handle large matrices such as adjacency matrices of OSN graphs. In this paper, we propose a random matrix approach to OSN data publishing, which achieves storage and computational efficiency by reducing dimensions of adjacency matrices and achieves differential privacy by adding a small amount of noise. Our key idea is to first project each row of an adjacency matrix into a low-dimensional space using random projection, and then perturb the projected matrix with random noise, and finally publish the perturbed and projected matrix. In this paper, we first prove that random projection plus random perturbation preserve differential privacy, and also that the random noise required to achieve differential privacy is small. We validate the proposed approach and evaluate the utility of the published data for three different applications, namely node clustering, node ranking, and node classification, using publicly available OSN graphs of Facebook, LiveJournal, and Pokec.

QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream Dissemination

Efficient event stream dissemination is a challenging problem in large-scale Online Social Network (OSN) systems due to the costly inter-server communications caused by the per-user view data storage. To solve the problem, previous schemes mainly explore the structures of social graphs to reduce the inter-server traffic. Based on the observation of high cluster coefficients in OSNs, a state-of-the-art social piggyback scheme can save redundant messages by exploiting an intrinsic hub-structure in an OSN graph for message piggybacking. Essentially, finding the best hub-structure for piggybacking is equivalent to finding a variation of the densest sub-graph. The existing scheme computes the best hub-structure by iteratively removing the node with the minimum weighted degree. Such a scheme incurs a worst computation cost of $O(n^2)$ O ( n 2 ) , making it not scalable to large-scale OSN graphs. Using alternative hub-structure instead of the best hub-structure can speed up the piggyback assignment. However, they greatly sacrifice the communication efficiency of the assignment schedule. Different from the existing designs, in this work, we propose a QuickPoint algorithm, which removes a fraction of nodes in each iteration in finding the best hub-structure. We mathematically prove that QuickPoint converges in $O(log_an) (a>1)$ O ( l o g a n ) ( a > 1 ) iterations in finding the best hub-structure for efficient piggyback. We implement QuickPoint in parallel atop Pregel, a vertex-centric distributed graph processing platform. Comprehensive experiments using large-scale data from Twitter and Flickr show that our scheme is 38.8× more efficient compared to existing schemes.

Privacy preserving solution to prevent classification inference attacks in online social networks

In order to improve their business solutions the data holders often release the social network data and its structure to the third party. This data undergo node and attribute anonymisation before its release. This however does not prevent the users from inference attacks which an un-trusted third party or an adversary would carry out at their end by analysing the structure of the graph. Therefore, there is an utmost necessity to not only anonymise the nodes and their attributes but also to anonymise the edge sets in the released social network graph. Anonymising involves perturbing the actual data which results in utility loss. Ensuring utility and preserving privacy are inversely proportional to each other and is a challenging task. In this work we have proposed, implemented and verified an efficient utility based privacy preserving solution to prevent the third party inference attacks for an online social network graph.

Learning to lurker rank: an evaluation of learning-to-rank methods for lurking behavior analysis

While being long researched in social science and computer–human interaction, lurking behaviors in online social networks (OSNs) have been computationally studied only in recent years. Remarkably, determining lurking behaviors has been modeled as an unsupervised, eigenvector-centrality-based ranking problem, and it has been shown that lurkers can effectively be ranked according to the link structure of an OSN graph. Although this approach has enabled researchers to overcome the lack of ground-truth data at a large scale, the complexity of the problem hints at the opportunity of learning from past lurking experiences as well as of using a variety of behavioral features, including any available, possibly platform-specific information on the activity and interaction of lurkers in an OSN. In this paper, we leverage this opportunity in a principled way, by proposing a machine-learning framework which, once trained on lurking/non-lurking examples from multiple OSNs, allows us to predict the ranking of unseen lurking behaviors, ultimately enabling the prioritization of user engagement tasks. Results obtained on 23 network datasets by state-of-the-art learning-to-rank methods, using different optimization and evaluation criteria, show the significance of the proposed approach.

Enhanced Audit Strategies for Collaborative and Accountable Data Sharing in Social Networks

Data sharing and access control management is one of the issues still hindering the development of decentralized online social networks (DOSNs), which are now gaining more research attention with the recent developments in P2P computing, such as the secure public ledger–based protocols (Blockchains) for monetary systems. In a previous work, we proposed an initial audit–based model for access control in DOSNs. In this article, we focus on enhancing the audit strategies and the privacy issues emerging from records kept for audit purposes. We propose enhanced audit and collaboration strategies, for which experimental results, on a real online social network graph with simulated sharing behavior, show an improvement in the detection rate of bad behavior of more than 50% compared to the basic model. We also provide an analysis of the related privacy issues and discuss possible privacy-preserving alternatives.

Estimation of structural properties of online social networks at the extreme

Sampling is a commonly used technique for studying structural properties of online social networks (OSNs). Due to privacy, business, and performance concerns, OSN service providers impose limitations on data access for third parties. The implication of this practice is that one needs to come up with an applicable sampling scheme that can function under these limitations to efficiently estimate structural properties of interest. In this paper, we study how accurately some important properties of graphs can be estimated under a limited data access model. More specifically, we consider random neighbor access (RNA) model as a rather limited data access model in OSNs. In the RNA model, the only query available to get data from the studied graph is the random neighbor query which returns the id of a random neighbor for a given vertex id. We propose various sampling schemes and estimators for average degree and network size under the RNA model. We conduct extensive experiments on both real world OSN graphs and synthetic graphs (1) to measure the performance of the proposed estimators and (2) to identify the factors affecting the accuracy of our estimators. We find that while the average degree estimators can make accurate estimations with reasonable sample sizes despite the extreme data access limitations of the RNA model, network size estimators require quite large sample sizes for accurate estimations.

A data driven anonymization system for information rich online social network graphs

In recent years, online social networks have become a part of everyday life for millions of individuals. Also, data analysts have found a fertile field for analyzing user behavior at individual and collective levels, for academic and commercial reasons. On the other hand, there are many risks for user privacy, as information a user may wish to remain private becomes evident upon analysis. However, when data is anonymized to make it safe for publication in the public domain, information is inevitably lost with respect to the original version, a significant aspect of social networks being the local neighborhood of a user and its associated data. Current anonymization techniques are good at identifying risks and minimizing them, but not so good at maintaining local contextual data which relate users in a social network. Thus, improving this aspect will have a high impact on the data utility of anonymized social networks. Also, there is a lack of systems which facilitate the work of a data analyst in anonymizing this type of data structures and performing empirical experiments in a controlled manner on different datasets. Hence, in the present work we address these issues by designing and implementing a sophisticated synthetic data generator together with an anonymization processor with strict privacy guarantees and which takes into account the local neighborhood when anonymizing. All this is done for a complex dataset which can be fitted to a real dataset in terms of data profiles and distributions. In the empirical section we perform experiments to demonstrate the scalability of the method and the improvement in terms of reduction of information loss with respect to approaches which do not consider the local neighborhood context when anonymizing.

Fuzzy soft set decision-making model for social networking sites

The 'social media' has become synonymous of today's generation. Approximately two third of Indians online spend time on different social networking sites like Facebook, Twitter, YouTube, Whatsapp, Qzone, Google+, Snapchat, Pinterest, etc. Interaction, live chat, status updates, image- as well as video-sharing are few of the major aspects that play a role in the popularity of social media. This popularity provides an opportunity to study and analyse the characteristics of online social network graphs at large scale. Understanding these graphs is important to improve current systems and to design new means to determine them by important parameters such as: security, reliability, value added features, connectivity to other online social networks, etc. Hence, social network analysis (SNA) is becoming a vital tool for researchers, but all the necessary information is often available in a distributed environment. This paper presents fuzzy soft set decision-making model, which gives a new hypothesis for determining the popular social networking sites by involving significant parameters. The model has applied fuzzy soft set theory on 14 significant parameters to predict the popularity of social networking sites. The experimental result shows that the FSS decision-making model provides a new algorithm which is to determine the most popular networking site.

Unbiased Characterization of Node Pairs over Large Graphs

Characterizing user pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large-scale nature of such networks, it is infeasible to enumerate all user pairs and thus sampling is used. In this article, we show that it is a great challenge for OSN service providers to characterize user pair relationships, even when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling (UVS) and random walk (RW)) are naively applied, they can introduce large biases, particularly for estimating similarity distribution of user pairs with constraints like existence of mutual neighbors, which is important for applications such as identifying network homophily. Estimating statistics of user pairs is more challenging in the absence of the complete topology information, as an unbiased sampling technique like UVS is usually not allowed and exploring the OSN graph topology is expensive. To address these challenges, we present unbiased sampling methods to characterize user pair properties based on UVS and RW techniques. We carry out an evaluation of our methods to show their accuracy and efficiency. Finally, we apply our methods to three OSNs—Foursquare, Douban, and Xiami—and discover that significant homophily is present in these networks.

Exploration and Optimization Of Friends’ Connections In Social Networks

One paragraph only. Over the past few years, the rapid growth and the exponential use of social digital media has led to an increase in popularity of social networks and the emergence of social computing. In general, social networks are structures made of social entities (e.g., individuals) that are linked by some specific types of interdependency such as friendship. Most users of social media (e.g., Facebook, LinkedIn, MySpace, Twitter, Flickr, YouTube) have many linkages in terms of friends, connections, and/or followers. Among all these linkages, some of them are more important than others. This paper discusses related work on social networks and method use in crawling online social network graph.

A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network

Social relationships and networking are key components of human life. Social network analysis provides both a visual and a mathematical analysis of human relationships. Recently, online social networks have gained significant popularity. This popularity provides an opportunity to study the characteristics of online social network graphs at large scale. An online social network graph consists of people as nodes who interact in some way such as members of online communities sharing information using relationships among them. In this paper a state of the art survey of the works done on community tracking in social network. The main goal is to provide a road map for researchers working on different measures for tracking communities in Social Network.

Online social network profile data extraction for vulnerability analysis

The increase in social computing has provided the situation where large amounts of personal information are being posted online. This makes people vulnerable to social engineering attacks because their personal details are readily available. Our automated approach for personal data extraction was developed to extract personal details and top friends from MySpace profiles and place them into a repository. An online social network graph was generated from the repository data where nodes represent peoples' profiles. Analysis was carried out into what factors affect node vulnerability. The graph analysis identified structural features of the nodes, e.g., clustering coefficient, indegree and outdegree, which contribute towards vulnerability. From this, it was found that the number of neighbours and the clustering coefficient were major factors in making a node vulnerable because of the potential to spread personal details around the network. These results provide a good foundation for future work on online vulnerability in online social networks (OSNs).