Social Data Offloading in D2D-Enhanced Cellular Networks by Network Formation Games

Recently, cellular networks are severely overloaded by social-based services, such as YouTube, Facebook and Twitter, in which thousands of clients subscribe a common content provider (e.g., a popular singer) and download his/her content updates all the time. Offloading such traffic through complementary networks, such as a delay tolerant network formed by device-to-device (D2D) communications between mobile subscribers, is a promising solution to reduce the cellular burdens. In the existing solutions, mobile users are assumed to be volunteers who selfishlessly deliver the content to every other user in proximity while moving. However, practical users are selfish and they will evaluate their individual payoffs in the D2D sharing process, which may highly influence the network performance compared to the case of selfishless users. In this paper, we take user selfishness into consideration and propose a network formation game to capture the dynamic characteristics of selfish behaviors. In the proposed game, we provide the utility function of each user and specify the conditions under which the subscribers are guaranteed to converge to a stable network. Then, we propose a practical network formation algorithm in which the users can decide their D2D sharing strategies based on their historical records. Simulation results show that user selfishness can highly degrade the efficiency of data offloading, compared with ideal volunteer users. Also, the decrease caused by user selfishness can be highly affected by the cost ratio between the cellular transmission and D2D transmission, the access delays, and mobility patterns

Securing Heterogeneous Privacy Protection in Social Network Records based Encryption Scheme

This survey places of interest the major issues concerning privacy and security in online social networks. Firstly, we discuss investigate that aims to protect user data from the an assortment of attack vantage points together with other users, advertisers, third party request developers, and the online social arrangement provider itself. Next we cover social network supposition of user attributes, locate hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. Social Networking sites go upwards since of all these reasons. In recent years indicates that for many people they are now the mainstream communication knowledge. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems threats and attacks such as revelation of information, identity thefts etc. Privacy practice in social networking sites often appear convoluted as in sequence sharing stands in discord with the need to reduce disclosure-related abuses. Facebook is one such most popular and widely used Social Networking sites which have its own healthy set of Privacy policy

Google+ or Google-?: Dissecting the Evolution of the New OSN in its First Year

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?". This paper tackles the above question by presenting a detailed characterization of G+ based on large scale measurements. We identify the main components of G+ structure, characterize the key features of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics with other major OSNs. We show that despite the dramatic growth in the size of G+, the relative size of LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity. To our knowledge, our study offers the most comprehensive characterization of G+ based on the largest collected data sets.Comment: WWW 201

Why Do People Use Social Networks?

Online social networking (OSN) sites such as Facebook, YouTube, and Twitter are among the most popular sites around the World. In the case of Mexico, these sites are considered to be in the top. Users have different tools for reading and sharing information with his friends and/or contacts and for searching for new users that might have common interests. These sites have changed the way people get connected to each other on the web. The popularity of these sites is represented on its registered users: as today Facebook has about 500 million, and Twitter about 175 million. As these numbers grow, we believe that there is a great opportunity to study critical characteristics of online social network in order to identify the key success factors based on end users perception. Identifying such factors would provide important information for improving current information systems by including social software characteristics as well as to design new high impact applications for online social networks. This paper presents the results obtained through a focus group study to identify the most important issues and perceptions about OSN. Results give us an idea where and what we should be doing for future research in the subject

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin

Heterogeneous characters modeling of instant message services users’ online behavior

Research on temporal characteristics of human dynamics has attracted much attentions for its contribution to various areas such as communication, medical treatment, finance, etc. Existing studies show that the time intervals between two consecutive events present different non-Poisson characteristics, such as power-law, Pareto, bimodal distribution of power-law, exponential distribution, piecewise power-law, et al. With the occurrences of new services, new types of distributions may arise. In this paper, we study the distributions of the time intervals between two consecutive visits to QQ and WeChat service, the top two popular instant messaging services in China, and present a new finding that when the value of statistical unit T is set to 0.001s, the inter-event time distribution follows a piecewise distribution of exponential and power-law, indicating the heterogeneous character of IM services users’ online behavior in different time scales. We infer that the heterogeneous character is related to the communication mechanism of IM and the habits of users. Then we develop a combination model of exponential model and interest model to characterize the heterogeneity. Furthermore, we find that the exponent of the inter-event time distribution of the same service is different in two cities, which is correlated with the popularity of the services. Our research is useful for the application of information diffusion, prediction of economic development of cities, and so on.National Natural Science Foundation (China) (61201153)National Basic Research Program of China (973 Program) (Grant 2012CB315805)CCF Venus Research Project (CCF-VenustechRP2016004)Jiangsu Industrial Technology Research Institute. Institute of Future Networks Technology (BY2013095-2-16

Towards inferring communication patterns in online social networks

Grup de recerca: Security of Networks and Distributed Applications (SENDA)The separation between the public and private spheres on online social networks is known to be, at best, blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the online social network (OSN) provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this article, we study the relationship between private communication patterns and publicly available OSN data. Such a relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key toward developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision