Attack vector analysis and privacy-preserving social network data publishing

This paper addresses the problem of privacy-preserving data publishing for social network. Research on protecting the privacy of individuals and the confidentiality of data in social network has recently been receiving increasing attention. Privacy is an important issue when one wants to make use of data that involves individuals' sensitive information, especially in a time when data collection is becoming easier and sophisticated data mining techniques are becoming more efficient. In this paper, we discuss various privacy attack vectors on social networks. We present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. This study provides a summary of the current state-of-the-art, based on which we expect to see advances in social networks data publishing for years to come.

Privacy threat analysis of social network data

Social network data has been increasingly made publicly available and analyzed in a wide spectrum of application domains. The practice of publishing social network data has brought privacy concerns to the front. Serious concerns on privacy protection in social networks have been raised in recent years. Realization of the promise of social networks data requires addressing these concerns. This paper considers the privacy disclosure in social network data publishing. In this paper, we present a systematic analysis of the various risks to privacy in publishing of social network data. We identify various attacks that can be used to reveal private information from social network data. This information is useful for developing practical countermeasures against the privacy attacks.

The social network virtual design studio : integrated design learning using blended learning environments

Online communications, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted to. Social Networks (SN), as instruments for communication, have provided a potentially fruitful operative base for VDS. These technologies transfer communication, leadership, democratic interaction, teamwork, social engagement and responsibility away from the design tutors to the participants. The implementation of Social Network VDS (SNVDS) moved the VDS beyond its conventional realm and enabled students to develop architectural design that is embedded into a community of learners and expertise both online and offline. Problem-based learning (PBL) becomes an iterative and reflexive process facilitating deep learning. The paper discusses details of the SNVDS, its pedagogical implications to PBL, and presents how the SNVDS is successful in enabling architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, knowledge and construction with a rich learning experience.

Twitter tsunami early warning network : a social network analysis of Twitter information flows

In the aftermath of earthquakes, tsunamis, such as the 2011 Great East Japan Tsunami, caused enormous damage around the world. With the extreme disaster events of the past, nations improved disaster preparedness and response through sensors and tsunami early warning systems. Even with system usage, however, governments still need to warn the targeted citizens – who may be anywhere within the vulnerable areas – of predicted tsunami and ordered mass evacuations within a very limited lead time. While social media research is on the rise outside the domain of social networking, very little is written about Twitter use for tsunami early warning. In this research, therefore, we examined the utility of Twitter as a tsunami early warning network, which engages citizens and disaster management agencies in diffusing disaster information. We conducted a social network analysis of Twitter information flows among the central disaster warning agency’s Twitter followers during the 2012 Indonesia Earthquake.

Virtual design studio within a blended social network

Online interactions, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted. Social Networks (SN), as instruments for communication, have provided a potentially fruitful operative base for VDS. These technologies transfer communication, leadership, democratic interaction, teamwork, social engagement and responsibility away from the design tutors to the participants. The implementation of a Social Network VDS (SNVDS) moved the VDS beyond its conventional realm and enabled students to develop architectural design that is embedded into a community of learners and their expertise both online and offline. Problem-based learning (PBL) becomes an iterative and reflexive process facilitating deep learning. The paper discusses details of the SNVDS, its pedagogical implications to PBL, and presents how the SNVDS is successful in empowering architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, knowledge and construction with a rich learning experience.

Silences of ethical practice: dilemmas for researchers using social media

Social media, such as social network sites and blogs, are increasingly being used as core or ancillary components of educational research, from recruitment to observation and interaction with researchers. However, this article reveals complex ethical dilemmas surrounding consent, traceability, working with children, and illicit activity that we have faced as education researchers for which there is little specific guidance in the literature. We believe that ethical research committees cannot, and should not, be relied upon as our ethical compass as they also struggle to deal with emerging technologies and their implications. Consequently, we call for researchers to report on the ethical dilemmas in their practice to serve as a guide for those who follow. We also recommend considering research ethics as an ongoing dialogical process in which the researcher, participants, and ethics committee work together in identifying potential problems as well as finding ways forward.

Using social network analysis to identify key child care center staff for obesity prevention interventions: a pilot study

Introduction. Interest has grown in how systems thinking could be used in obesity prevention. Relationships between key actors, represented by social networks, are an important focus for considering intervention in systems. Method. Two long day care centers were selected in which previous obesity prevention programs had been implemented. Measures showed ways in which physical activity and dietary policy are conversations and actions transacted through social networks (interrelationships) within centers, via an eight item closed-ended social network questionnaire. Questionnaire data were collected from (17/20; response rate 85%) long day care center staff. Social network density and centrality statistics were calculated, using UCINET social network software, to examine the role of networks in obesity prevention. Results. “Degree” (influence) and “betweeness” (gatekeeper) centrality measures of staff inter-relationships about physical activity, dietary, and policy information identified key players in each center. Network density was similar and high on some relationship networks in both centers but markedly different in others, suggesting that the network tool identified unique center social dynamics. These differences could potentially be the focus of future team capacity building. Conclusion. Social network analysis is a feasible and useful method to identify existing obesity prevention networks and key personnel in long day care centers.

Analysing stakeholder-associated risks in green buildings : a social network analysis method

In the research of risk associated with developing energy and water efficient green buildings, previous studies had mainly focused on "what the risks are and how the risks may impact on project objectives", which were from an inward looking self-perspective and treated the risks in isolation from one another. While intensive research efforts have been dedicated to risk identification, assessment, classification, prioritisation and mitigation, a research gap exists, that is previous studies had ignored the fact that most risks are interrelated and associated with internal or external project stakeholders. To remedy the gap, this current research developed and presented a SNA (Social Network Analysis) based stakeholder-associated risk analysis method to assess risks in green buildings and the interactions between the risks. A case study has been conducted to demonstrate and validate this method. This research contributes to the development of a new theory to model the interdependent and interactive relationships of risks by using SNA as a methodology. This research should broaden project managers' awareness of the influential risks in green building and enhance their ability to perceive, understand, assess, and mitigate the risks in an effective and efficient way, thereby achieving higher performance in strategic risk management and stakeholder communication in green building development.

Modeling propagation dynamics of social network worms

Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious- recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.

Preserving utility in social network graph anonymization

To protect from privacy disclosure, the social network graph is modified in order to hide the information that potentially be used to disclose person's identity. However, when the social network graph is changed, it is a great challenge to balance between the privacy gained and the loss of data utility. In this paper, we address this problem. We propose a new graph topological-based metric to improve utility preservation in social network graph anonymization. We compare the proposed approach with the amount-of-edge-change metric that popularly used in most of previous works. Experimental evaluation shows that our approach generates anonymized social network with improved utility preservation.

A multilevel social network perspective on IT adoption

Adoption of technologies has long been a key area of research in the information systems (IS) discipline, and researchers have thus been interested in the attributes, beliefs, intentions, and behaviors of individuals and organisations that could explain information and communication technology (ICT) adoption. The focal unit of adoption has mainly been individuals and organisations, however, research at group or social network level as well as the inter-organizational level have recently gained increased interest from IS researchers. This recent focus supports the view of the world as being the sum of all relations. Various social network theories exist that seek to emphasize different proficiencies of social networks and explain theoretical mechanisms for behavior in social networks. The core idea of these theories is that social networks are valuable, and the relations among actors affect the behavior of individuals, groups, organizations, industries, and societies. IS researchers have also found that social network theory can help explain technology adoption. Some researchers, in addition, acknowledge that most adoption situations involve phenomena occuring at multiple levels, yet most technology adoption research applies a single level of analysis. Multilevel research can address the levels of theory, measurement, and analysis required to fully examining research questions. This paper therfore adapts the Coleman diagram into the Multi-level Framework of Technology. Adoption in order to explain how social network theory, at the individual and social network level, can help explain adoption of ICT. As Coleman (1990) attempts to create a link between the micro and macro level in a holistic manner, his approach is applicable in explaining ICT adoption

Early adoption of mobile devices : a social network perspective

As mobile devices have become the personal information-processing interface of choice, many individuals seem to swiftly follow fashion. Yet, the literature is silent on how early adopters of mobile devices overcome uncertainties related to shifts in technology. Based on purposive sampling, this paper presents detailed insights into why and how five closely related individuals made the decision to adopt the iPhone before it was available through traditional supply chains. Focusing on the role played by social networks, we analyze how adoption threshold, opinion leaders, social contagion, and social learning shaped adoption behaviors and outcomes. The analyses confirm that network structures impact the early decision to accept the iPhone; they show that when facing uncertainty, adoption decisions emerged as a combined result of individual adoption reflections and major influences from the social network as well as behaviors observed within the network, and, they reveal interesting behaviors that differed from expectations. In conclusion, we discuss implications for both theory and practice.