Understanding the Intention of Using Mobile Social Networking Apps Across Cultures

Understanding the user acceptance of mobile social networking apps in different cultures can provide powerful insights for managers and marketers of social networking apps to develop effective globalized and localized strategies to attract users worldwide. Following the theory of planned behavior, this study develops a research model of privacy concern (PC), privacy risk (PR), and perceived enjoyment (PE) as attitudinal beliefs, subjective norm (SN) as normative belief, and smartphone self-efficacy (SE) as control belief to understand users’ intention to use mobile social networking apps. In particular, the impact of culture was investigated, considering the user base of mobile social networking apps is distributed globally and culturally diversified, and cultural values have direct impact on behavior. The research model was validated by survey data collected from 151 participants in the U.S. and 170 participants in South Korea. The data analysis results show that perceived enjoyment and subjective norm are the most important drivers behind users’ intention to use mobile social networking apps for both countries. No significant difference was found for the effects of privacy risk and subjective norm upon users’ intention to use mobile social networking apps across cultures. Implications of the findings upon theory and practice are discussed.     

Constraint-based and dedication-based mechanisms for encouraging online self-disclosure: Is personalization the only thing that matters?

Consumer-generated self-disclosure is better than firm-generated advertising and sales reports in increasing contact opportunities and also more credible for firms to foster alignment with future market expectations. Previous research mostly assesses online self-disclosure from the rational approach of anticipated benefits and privacy risks without considering the “privacy paradox” phenomenon (users behave contrarily to privacy concern) in social networking sites (SNSs). We develop a theoretical model, grounded in constraint-based (lock-in) and dedication-based (trust-building) mechanisms and social identity theory, to predict online self-disclosure. We test the proposed theoretical model by surveying 395 consumers with participation experience in an online SNS. Different from the rational approach behind personalization, we advance knowledge on how to apply social identity, as well as constraint-based and dedication-based mechanisms, to motivate online self-disclosure induced by consumers. We provide theoretical and practical insights based on our research findings for managing the motivational mechanisms of online self-disclosure.     

Internet social network communities: Risk taking,trust, and privacy concerns

Individuals communicate and form relationships through Internet social networking websites such as Facebook and MySpace. We study risk taking, trust, and privacy concerns with regard to social networking websites among 205 college students using both reliable scales and behavior. Individuals with profiles on social networking websites have greater risk taking attitudes than those who do not; greater risk taking attitudes exist among men than women. Facebook has a greater sense of trust than MySpace. General privacy concerns and identity information disclosure concerns are of greater concern to women than men. Greater percentages of men than women display their phone numbers and home addresses on social networking websites. Social networking websites should inform potential users that risk taking and privacy concerns are potentially relevant and important concerns before individuals sign-up and create social networking websites.     

Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry

Increasingly, millions of people, especially youth, post personal information in online social networks (OSNs). In September 2006, one of the most popular sites—Facebook.com—introduced the features of News Feed and Mini Feed, revealing no more information than before, but resulting in immediate criticism from users. To investigate the privacy controversy, we conducted a survey among 172 current Facebook users in a large US university to explore their usage behaviors and privacy attitudes toward the introduction of the controversial News Feed and Mini Feed features. We examined the degree to which users were upset by the changes, explored the reasons as to why, and examined the influences of the News Feed privacy outcry on user behavior changes. The results have demonstrated how an easier information access and an “illusory” loss of control prompted by the introduction of News Feed features, triggered users’ privacy concerns. In addition to enhancing our theoretical understanding of privacy issues in the online social networks, this research is also potentially useful to privacy advocates, regulatory bodies, service providers, and marketers to help shape or justify their decisions concerning the online social networks.     

Understanding how big data leads to social networking vulnerability

Although the term “Big Data” is often used to refer to large datasets generated by science and engineering or business analytics efforts, increasingly it is used to refer to social networking websites and the enormous quantities of personal information, posts, and networking activities contained therein. The quantity and sensitive nature of this information constitutes both a fascinating means of inferring sociological parameters and a grave risk for security of privacy. The present study aimed to find evidence in the literature that malware has already adapted, to a significant degree, to this specific form of Big Data. Evidence of the potential for abuse of personal information was found: predictive models for personal traits of Facebook users are alarmingly effective with only a minimal depth of information, “Likes”, It is likely that more complex forms of information (e.g. posts, photos, connections, statuses) could lead to an unprecedented level of intrusiveness and familiarity with sensitive personal information. Support for the view that this potential for abuse of private information is being exploited was found in research describing the rapid adaptation of malware to social networking sites, for the purposes of social engineering and involuntary surrendering of personal information.     

社交网络用户浏览轨迹信息隐私保护方法仿真

随着网络应用的广泛普及,QQ、微信、YY语音、陌陌等社交软件走进千家万户,但社交网络用户浏览轨迹信息隐私保护问题也随之而来。由于社交网络平台安全机制存在漏洞,抵御网络攻击性能不强,使社交网络用户信息纷纷泄露。针对问题根源,提出ACP用户隐私信息防护系统,建立社交网络用户真空登陆模块(VM)、通讯信息密码文模块(RDT)及信息储存保护墙模块(LDM)一体化ACP用户隐私信息防护系统,从根源保护社交网络用户浏览轨迹信息的隐私安全。通过数据模拟仿真实验证明提出的ACP用户隐私信息防护系统,对社交网络用户浏览轨迹信息隐私保护具有可用性与有效性。     

An Empirical Study of Home IoT Services in South Korea: The Moderating Effect of the Usage Experience

This article examines the responses of users to home Internet of Things (IoT) services in South Korea, which is taking progressive steps in the field of IoT. It is important to investigate the user’s response because home IoT users are the core users of the IoT business. To this end, the research model includes two trust constructs — “trust in the service provider” and “institutional trust”; two risk constructs — “perceived security risk” and “perceived privacy risk”; and “perceived benefit” construct. This study has two main objectives: (1) to establish the functional relationship among the five constructs listed above; (2) to examine the moderating role of home IoT usage experience in these relationships. The study first reviews the literature on home IoT services and describes the Korean situation. Data were collected from residents living in a smart apartment complex. They were made aware of not only the benefits of home IoT but also the security and privacy risks before they moved into their new homes. The research model was empirically analyzed with structural equation modeling (SEM) using Amos 22.0. The results show that (1) “trust in the service provider” negatively influences “perceived security risk” and “perceived privacy risk” while “institutional trust” does not have a significant influence on them, (2) “perceived security risk” and “perceived privacy risk” negatively influence “perceived benefit,” and (3) “trust in service provider” does not directly influence “perceived benefit” while “institutional trust” has a positive and direct influence on it. In addition, there is a significant moderating effect of home IoT usage experience on some paths. Finally, the study’s findings and limitations are discussed, and potential avenues for future research are suggested.     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

Private personalized social recommendations in an IPTV system

In our connected world, recommender systems have become widely known for their ability to provide expert and personalize referrals to end-users in different domains. The rapid growth of social networks and new kinds of systems so called “social recommender systems” are rising, where recommender systems can be utilized to find a suitable content according to end-users' personal preferences. However, preserving end-users' privacy in social recommender systems is a very challenging problem that might prevent end-users from releasing their own data, which detains the accuracy of extracted referrals. In order to gain accurate referrals, social recommender systems should have the ability to preserve the privacy of end-users registered in this system. In this paper, we present a middleware that runs on end-users' Set-top boxes to conceal their profile data when released for generating referrals, such that computation of recommendation proceeds over the concealed data. The proposed middleware is equipped with two concealment protocols to give users a complete control on the privacy level of their profiles. We present an IPTV network scenario and perform a number of different experiments to test the efficiency and accuracy of our protocols. As supported by the experiments, our protocols maintain the recommendations accuracy with acceptable privacy level.     

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today’s most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users’ understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.     

LotusNet: Tunable privacy for distributed online social network services

The evolution of the role of online social networks in the Web has led to a collision between private, public and commercial spheres that have been inevitably connected together in social networking services since their beginning. The growing awareness on the opaque data management operated by many providers reveals that a privacy-aware service that protects user information from privacy leaks would be very attractive for a consistent portion of users. In order to meet this need we propose LotusNet, a framework for the development of social network services relying on a peer-to-peer paradigm which supports strong user authentication. We tackle the trade-off problem between security, privacy and services in distributed social networks by providing the users the possibility to tune their privacy settings through a very flexible and fine-grained access control system. Moreover, our architecture is provided with a powerful suite of high-level services that greatly facilitates custom application development and mash up.     

A case study-based investigation of students’ experiences with social software tools

The term “social software” covers a range of tools which allow users to interact and share data with other users, primarily via the web. Blogs, wikis, podcasts and social networking websites are some of the tools that are being used in educational, social and business contexts. We have examined the use of social software in the UK further and higher education to collect evidence of the effective use of social software in student learning and engagement. We applied case study methodology involving educators and students from 26 initiatives. In this paper, we focus on the student experience: educational goals of using social software; benefits to the students; and the challenges they experience. Our investigations have shown that social software supports a variety of ways of learning: sharing of resources; collaborative learning; problem-based and inquiry-based learning; and reflective learning. Students gain transferable skills of team working, negotiation, communication and managing digital identities. Although these tools enhance a student's sense of community, the need to share and collaborate brings in additional responsibility and workload, which some students find inflexible and “forced”. Our findings show that students have concerns about usability, privacy and the public nature of social software tools for academic activities.     

TAPAS: Trustworthy privacy-aware participatory sensing

With the advent of mobile technology, a new class of applications, called participatory sensing (PS), is emerging, with which the ubiquity of mobile devices is exploited to collect data at scale. However, privacy and trust are the two significant barriers to the success of any PS system. First, the participants may not want to associate themselves with the collected data. Second, the validity of the contributed data is not verified, since the intention of the participants is not always clear. In this paper, we formally define the problem of privacy and trust in PS systems and examine its challenges. We propose a trustworthy privacy-aware framework for PS systems dubbed TAPAS, which enables the participation of the users without compromising their privacy while improving the trustworthiness of the collected data. Our experimental evaluations verify the applicability of our proposed approaches and demonstrate their efficiency.     

Social networking searching and privacy issues

The explosion of social networking sites has not only changed the way people communicate, but also added a new dimension to the way for searching or investigating people. As users share a wide variety of information on social networking sites, concerns are growing about organisations’ access to personally identifiable data and users are increasingly worried about privacy on social network sites. The main threat with data gathering is not only from where gathering it, but also where it goes afterwards. Neither social network sites providers nor the governments have any way to effectively protect users against privacy violations. However, a variety of efforts need to be explored to change the situation. Social network sites should continue work to strengthen privacy settings. Laws and policies should be improved to regulate the social networking searching in its legality, necessity and proportionality.     

Users' Satisfaction with Social Network Sites: A Self-Determination Perspective

When the online social networking market is no longer a “blue ocean,” retaining existing users and maintaining their satisfaction with the current social network site (SNS) become SNS providers' most important tasks. This study applies Self-Determination Theory and examines the relationship between trust, relatedness need, and users' satisfaction with SNSs. Using Facebook as the context, we tested our hypotheses with the student sample from a large state university in U.S. The results show that competence and benevolence trust beliefs positively influence relatedness need satisfaction; relatedness need satisfaction and relatedness need level significantly influence uses' satisfaction with SNSs. The theoretical and practical implications of this study are also discussed.     

User demographics prediction based on mobile data

Demographics prediction is an important component of user profile modeling. The accurate prediction of users’ demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users’ demographics, including “gender”, “job type”, “marital status”, “age” and “number of family members”, based on mobile data, such as users’ usage logs, physical activities and environmental contexts. The core idea is to build a supervised learning framework, where each user is represented as a feature vector and users’ demographics are considered as prediction targets. The most important component is to construct features from raw data and then supervised learning models can be applied. We propose a feature construction framework, CFC (contextual feature construction), where each feature is defined as the conditional probability of one user activity under the given contexts. Consequently, besides employing standard supervised learning models, we propose a regularized multi-task learning framework to model different kinds of demographics predictions collectively. We also propose a cost-sensitive classification framework for regression tasks, in order to benefit from the existing dimension reduction methods. Finally, due to the limited training instances, we employ ensemble to avoid overfitting. The experimental results show that the framework achieves classification accuracies on “gender”, “job” and “marital status” as high as 96%, 83% and 86%, respectively, and achieves Root Mean Square Error (RMSE) on “age” and “number of family members” as low as 0.69 and 0.66 respectively, under the leave-one-out evaluation.     

Self-reflection on privacy research in social networking sites

The increasing popularity of social networking sites has been a source of many privacy concerns. To mitigate these concerns and empower users, different forms of educational and technological solutions have been developed. Developing and evaluating such solutions, however, cannot be considered a neutral process. Instead, it is socially bound and interwoven with norms and values of the researchers. In this contribution, we aim to make the research process and development of privacy solutions more transparent by highlighting questions that should be considered. (1) Which actors are involved in formulating the privacy problem? (2) Is privacy perceived as a human right or as a property right on one’s data? (3) Is informing users of privacy dangers always a good thing? (4) Do we want to influence users’ attitudes and behaviours? (5) Who is the target audience? We argue that these questions can help researchers to better comprehend their own perspective on privacy, that of others, and the influence of the solutions they are developing. In the discussion, we propose a procedure called ‘tool clinics’ for further practical implementations.     

The Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network

The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by a unique set of cultural preferences—of which the “taste for privacy” may be only a small but integral part.