A game theoretic approach for modeling optimal data sharing on Online Social Networks

In the social media era, the ever-increasing utility of Online Social Networks (OSN) services provide a variety of benefits to users, organizations, and service providers. However, OSN services also introduce new threats and privacy issues regarding the data they are dealing with. For instance, in a reliable OSN service, a user should be able to set up his desired level of information sharing and securely manage sensitive data. Currently, few approaches exist that can model OSNs for the purpose, let alone a model the effects that attackers can have on these networks. In this work a novel OSN modeling approach is presented to fill the gap. This model is based on an innovative game-theoretic approach and it is analyzed both from a theoretical and simulation-oriented view. The game-theoretic model is implemented to analyze several attack scenarios. Honeytokens, which are an information security tool based upon deception, are defined and identified as a security tool that could help in OSN security. As the results show, there are several scenarios where OSN services are very vulnerable and hence more protection mechanisms should be provided to secure the data contained across these networks, including the use of honeytokens. In this work we introduce a novel OSN modeling approach for optimal data sharing based on innovative game theories, considering the states/optimal policies of data sharing on OSNs and possible confrontations between the attacker and the user. After we develop the theoretical framework, we conduct experiments, integrating our ideas with honeytokens in several attack scenarios. Finally, we analyze our experimental results and discuss recommendations based on the results.

Online social networking usage is growing rapidly, especially among at-risk populations, such as men who have sex with men (MSM). However, little research has studied the relationship between online social networking usage and sexual risk behaviors among at-risk populations. One hundred and eighteen Facebook-registered MSM (60.1% Latino, 28% African American; 11.9% other) were recruited from online (social networking websites and banner advertisements) and offline (local clinics, restaurants and organizations) venues frequented by minority MSM. Inclusion criteria required participants to be men who were 18 years of age or older, had had sex with a man in the past 12 months, were living in Los Angeles, and had a Facebook account. Participants completed an online survey on their social media usage and sexual risk behaviors. Results from a multivariable regression suggest that number of sexual partners met from online social networking technologies is associated with increased: 1) likelihood of having exchanged sex for food, drugs, or a place to stay within the past 3 months; 2) number of new partners within the past 3 months; 3) number of male sex partners within the past 3 months; and 4) frequency of engaging in oral sex within the past 3 months, controlling for age, race, education, and total number of sexual partners. Understanding the relationship between social media sex-seeking and sexual risk behaviors among at-risk populations will help inform population-focused HIV prevention and treatment interventions.

Recently, communication and sharing the information through online social network (OSN) grows very fast in day to day life. As user share important data through online social network, therefore providing security and privacy to the online social network becomes the challenging task for the researchers. Moreover, for increasing the social connections and get the information from the particular group of people user needs to search the new friends and add them in the friend list. Now a days there isa friend recommendation method which was used by many online social networks, this method recommended the friend list to the user. As the privacy of these friend recommendation methods is the most significance task for the researchers and for the online social networks. Basically an online social network focused on the millions people's profile to spend time on an online social network, also implementing an OSN clients social circles, by using recommended suggestions. In this system, the main focus is on privacy and security of the online social network. This system basically focuses to support the users of online social network by secure trust creation with a stranger. This is achieved with multi-hop recommendation process. In the proposed method, the system provides privacy to the online social network from which system is allowing users to enhance their social networks. Existing system used secure KNN algorithm for securing social network. By using KNN algorithm, distance based learning is unclear. It is sort of distance and their component to use for the best results and calculation is very expensive. To overcome this limitation and to improve the precision, the proposed frame-work makes use of secure social coordinate matching with a Naive Bayes classifier. Also the privcy of social coordinates is maintained with Pailier encryption algorithm. Experimental results demonstrate that the proposed system is secure and time efficient than the existing one. Memory consumption is also improved with the proposed system.

Hundreds of millions of people use online social networks (OSNs), and this use is growing rapidly. On the one hand, users of OSNs have created accounts, which include their personal information, interests, associations, postings, daily activities, sharing of social media, and so forth. In addition, users want to protect these information and activities. But on the other hand, they want to use the services, which are provided by OSNs in order to stay in touch with others. Therefore, for achieving this desire, users reveal their personal information intentionally or accidentally. Why? Because users of OSNs do not have adequate information that helps them in making security and privacy decisions effectively in addition to inadequate feedback about the implications of user decisions. This means users do not know what to reveal and how. In order to cope with this problem, a systematic literature review of the usable security and privacy on OSNs was performed. This review is an attempt to pursue the contours of research in OSNs. Therefore, this paper discusses the existing research work, highlights the main research challenges and existing solutions, identifies open research issues, and proposes some directions for future research in the usable security and privacy on OSNs.

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

Now days, the online social networks (OSN) have gained considerable popularity. More and more people use OSN to share their interests and make friends, also the OSN helps users overcome the geographical barriers. With the development of OSN, there is an important problem users have to face that is trust evaluation. Before user makes friends with a stranger, the user need to consider the following issues: Can a stranger be trusted? How much the stranger can be trusted? How to measure the trust of a stranger? In this paper, we take two factors, Degree and Contact Interval into consideration, which produce a new trust evaluation model (T-OSN). T-OSN is aimed to solve how to evaluate the trust value of an OSN user, also which is more efficient, more reliable and easy to implement. Base on our research, this model can be used in wide range, such as online social network (OSN) trust evaluation, mobile network message forwarding, ad hoc wireless networking, routing message on Internet and peer-to-peer file sharing network. The T-OSN model has following obvious advantages compare to other trust evaluate methods. First of all, it is not base on features of traditional social network, such as, distance and shortest path. We choose the special features of OSN to build up the model, that is including numbers of friends(Degree) and contact frequency(Contact Interval). These species features makes our model more suitable to evaluate OSN users trust value. Second, the formulations of our model are quite simple but effective. That means, to calculate the result by using our formulations will not cost too much resources. Last but not least, our model is easy to implement for an OSN website, because of the features that we used in our model, such as numbers of friends and contact frequency are easy to obtain. To sum up, our model is using a few resources to obtain a valuable trust value that can help OSN users to solve an important security problem, we believe that will be big step for development of OSN.

This paper aims to identify user-centric features to calculate the strength of social ties between Online Social Network (OSN) users, and models the same using Latent Space Model (LSM). The modeling approach processes a socio-centric user-set as the users are directly (friend) or indirectly (friend-of-friend) related to a seed (target) user, which makes it easier to identify social ties between users as compared to random sampling from a set of diverse OSN users. For a given user, interaction data up to two levels is modeled and analyzed to generate a user-centric social network. Eleven different features related to Facebook have been identified to calculate the strength of social ties between users. LSM is used to visualize relationships in user-centric historical data and to estimate the probability of social ties between OSN users. The users are plotted using LSM in a three-dimensional (3D) social space around a seed user, and a link probability function is devised to calculate the probability of link between any two users with respect to the persona of the seed user. A sphere of influence around each user demarcating its active influence area is also identified and discussed in this paper.

Trust can be employed for finding reliable information in Online Social Networks (OSNs). Since users in OSNs may intentionally change their behavior over time (in some cases for deceiving other users), modeling (pair-wise) trust relations in such complex environment is a challenging task. However, most of the existing trust prediction approaches assume that trust relations are fixed over time and they fail to capture the dynamic behavior of users in OSNs. In this paper, we propose a dynamic deep trust prediction model. As the impact of incidental emotions on trust has been proven in psychology studies, in this paper, we also study this impact on our trust prediction approach. First, we propose a novel deep structure that incorporates users' emotions and their textual contents in OSNs. Second, we use embeddings to represent the users and their self-descriptions provided. Finally, considering different time windows, we dynamically predict pair-wise trust relations. To evaluate our approach, we collected a large twitter dataset. The evaluation results demonstrate the effectiveness of our approach compared to the state-of-the-art approaches.

The popularization of Information and Communication Technologies allowed new forms of interaction, including Online Social Network (OSN) services. These services could share personal data with third-party organizations through Application Programming Interfaces (API), making it a complex task to observe the data flow. This research proposes a model to identify levels and elements available in the flow of personal data via API between organizations holding OSN services and external agents. It converged the recurrent mechanisms into providing data sets from the OSN services to external agents from a systematized reading of the technical documentation to establish a database model to fetch unitedly the possibility of storing information about the levels in which API provided personal data to external agents, applying the Entity-Relationship Model. Subsequently, it presents a Data Mart as a proof of concept for the proposed database model, intending to compare the ways of accessing the personal data attributes stored and, following, shared by the OSN services to external agents. It showed that the model could clarify the elements inherent in the data flow, allowing a more structured analysis, including the possibility of monitoring changes in the API by organizations over time.

With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud’s well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users’ preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

Online Social Networks (OSN) have become an integral part of our everyday lives. We express ourselves, share content and information with our friends and colleagues, or exchange messages. Yet, despite social communication being implicitly a distributed, decentralized way of information exchange that does not require any central entity to supervise the process, most OSN services are built in a central, monolithic fashion. This contradicts the idea of the social web, as proprietary and isolated walled gardens keep users from being able to freely choose an OSN platform provider or to effectively control their privacy. To enable users of the social web to remain in control of their social profiles and data, solutions were proposed that allow hosting one's social profile on independent servers, which then connect to each other in a loosely coupled fashion. Unfortunately, implicit network effects existing in large OSN services prevent users from migrating to alternative solutions thus ultimately hindering alternative OSN services to succeed in the social web. In this paper, we outline the main challenges that need to be addressed in order to allow decentralized OSN services to become real alternatives to well-established services such as Facebook or Twitter. The identified main challenges are coverage of typical, standard OSN functionality, a holistic and extensible interoperability protocol, support for data portability of entire social profiles, and support for platform-independent user identification. The paper then outlines Sonic, an open architecture for an open and heterogeneous network of independent OSN services, the Online Social Network Federation (OSNF), in which the identified challenges are addressed altogether.

The introduction of online social networking has allowed people across the world to share information with each other. It has risen to be one of the most popular forms of internet usage where almost everyone has created one or multiple types of social network accounts. Along with its great benefits has come many concerns though, mainly that the overabundance of information has created a plethora of privacy issues for not only the users of online social networks, but also for the company hosting the specific social network. Due to the wide range of privacy concerns associated with online social networks, it is incredibly difficult to tackle all the current possible concerns. In this thesis, we propose works to tackle two privacy issues associated with online social networks. These two privacy issues are: the friend search engine, and image content. Firstly we will introduce a new sub-graph approach to the friend search engine that removes the ability of attackers to gain more information of your friend list than your privacy settings allow. Secondly we will introduce a new privacy setting that allows users to define locations they do not wish their face to be seen in images. If an image is posted with their face in such a location, they will be privatized through facial replacement so that they are unrecognizable. The overall efficiency of these works will be tested so that their enhanced privacy does not cause usability issues if they are adopted by a social network site. These works allow users to remain more private while using social media and also help users to remain confident that their privacy is kept safe. These improvements not only help strengthen the privacy of users, but also help social network sites retain users that are more wary of privacy breaches online.

Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

With the development of Online Social Networks(OSNs), OSNs have been becoming very popular platforms to publish resources and to establish relationship with friends. However, due to the lack of prior knowledge of others, there are usually risks associated with conducting network activities, especially those involving money. Therefore, it will be necessary to quantify the trust relationship of users in OSNs, which can help users decide whether they can trust another user. In this paper, we present a novel method for evaluating trust in OSNs using knowledge graph (KG), which is the cornerstone of artificial intelligence. And we focus on the two contributions for trust evaluation in OSNs: (i) a novel method using RNN to quantify trustworthiness in OSNs, which is inspired by relationship prediction in KG; (ii) a Path Reliability Measuring algorithm(PRM) to decide the reliability of a path from the trustor to the trustee. The experiment result shows that our method is more effective than traditional methods.

People involved in business are increasingly aware of the online social phenomenon and its potential as a marketing tool. Building upon the marketing, social psychology, and information system literature, this study empirically examines a set of functional determinants derived from the technology acceptance model and socio-psychological determinants drawn from the theory of reasoned action and social cognitive theory that may be responsible for the motivations of participants in online social networks. We propose a model by integrating functional, social, and psychological factors in predicting attitude and online social network usage. The survey outcomes from a cross-sectional study among 387 Facebook users in Malaysia support the hypothesised model. This study contributes by extending the cognitive learning theories in predicting online social networking usage. Particularly, this research presents a different perspective on online social networking by exploring the mediating effect of attitude and the moderating role of individual characteristics. This study demonstrates that desirable changes in the perception of functional benefits, self-efficacy, and need for cognition as well as perceptions of social pressure from significant others might lead to corresponding changes in attitude towards online social networking. Results of the study support the model efficacy and predictive ability of the proposed integrative model which may serve as a theoretical foundation for future researchers interested in examining other virtual community engagements such as blogging, online advertising, and online public relations activities.