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Enormous popularity of Social Networking Sites has introduced a great number of privacy risks. Even the most popular of all the social networking sites have characterized access control policies in terms of explicit tracking of the interpersonal relationships between the subjects, objects and their inter relation. In this paper we present a novel paradigm that accounts for a secure, yet sociable information flow model based on access control policies. We took advantage of real time success of the access control security policies in operating systems by implementing them on online social networks at the mandatory level so that the user's privacy does not have to be at stake by the growth of social network and activities or by the level of user understanding of the privacy settings provided by the social networking sites based on discretionary access control. We used Facebook and Google+ as case study and implemented the security policy in SecureWall to mitigate possible privacy leakage scenarios observed. We have implemented Chinese wall policy for community level privacy, Bell la-Padulla access control model to assure confidentiality to the user and Biba Access control model for providing Integrity. Since Bell la-Padulla and Biba models are basically meant to serve military security and therefore can risk sociability, we have combined the two models using Lipner Security Matrix in order to provide security without risking sociability. Our research can be adopted by online social networking sites for the mandatory level security especially for social networking in organizational specific activities.

The phenomenon of online social networking during the age of the web creates an era known as the 'Online Social Network Era'. Whilst the advantages of the online social network are numerous, the drawbacks of online social network are also worrying. The explosion of the use of online social networks creates avenues for cyber criminals to commit crimes online, due to the rise of information technology and Internet use, which results in the growth of the Internet society which includes the children. The children, who are in need of 'extra' protection, are among the community in the online social network, and they are exposed to the cyber crimes which may be committed against them. This article seeks to explore and analyse the position on the protection of the children in the online society; and the focus is in Malaysia while other jurisdictions are referred as source of critique. The position in Malaysia is looked into before the introduction of the Sexual Offences Against Children Act 2017. It is found that, in the Online Social Network Era, there are inadequate protections for children in the Malaysian legal framework before the introduction of the Act. The effectiveness of the Act which is already passed by the Parliament but yet to be enforced, is yet to be seen.

This book provides insights into the structural properties of personal online social networks and the mechanisms underpinning human online social behavior. As the availability of digital communication data generated by social media is revolutionizing the field of social networks analysis. It discusses the use of large- scale datasets to study the structural properties of online ego networks, to compare them with the properties of general human social networks, and to highlight additional properties. Users will find the data collected and conclusions drawn useful during design or research service initiatives that involve online and mobile social network environments. It presents quantitative evidence of the Dunbar's number in online environments and discusses original structural and dynamic properties of human social network through OSN analysis. --

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. ; [EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures. ; This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498. ; Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723 ; S ; 143631 ; 143645 ; 7

Recently, there has been strong interest in measuring influence in online social networks. Different measures have been proposed to predict when individuals will adopt a new behavior, given the influence produced by their friends. In this article, we show that one can achieve significant improvement over these measures, extending them to consider a pair of time constraints that provide a better proxy for social influence. By conducting an engineering study that investigates retweet networks from Twitter and Sina Weibo datasets, we tune those two parameters while we examine the correlation between influence and the probability of adoption, as well as the ability to predict adoption, estimating the real susceptibility and influence to which microblog users are dynamically subjected. Although there are limitations about using retweets to analyze social influence, our results show that for the simple count of active neighbors, its correlation with the probability of adoption is boosted up to 518.75%, whereas similar gains are observed for the other influence measures analyzed. We also obtain up to 18.89% improvement in
F
1 score when compared to recent machine learning techniques that aim to predict adoption, enabling practical use of the corresponding concepts for social influence applications.

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., "People you may know" or "Whom to follow" suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

143631 143645 7 ; S ; (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. [EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures. This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498. Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723

AbstractThe use of online social networks (OSNs) has increasingly attracted attention from scholars' in different disciplines. Recently, student behaviors in online social networks have been extensively examined. However, limited efforts have been made to evaluate and systematically review the current research status to provide insights into previous study findings. Accordingly, this study conducted a systematic literature review on student behavior and OSNs to explicate to what extent students behave on these platforms. This study reviewed 104 studies to discuss the research focus and examine trends along with the important theories and research methods utilized. Moreover, the Stimulus-Organism-Response (SOR) model was utilized to classify the factors that influence student behavior. This study's results demonstrate that the number of studies that address student behaviors on OSNs have recently increased. Moreover, the identified studies focused on five research streams, including academic purpose, cyber victimization, addiction, personality issues, and knowledge sharing behaviors. Most of these studies focused on the use and effect of OSNs on student academic performance. Most importantly, the proposed study framework provides a theoretical basis for further research in this context.