scholarly journals A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage

2018 ◽  
Vol 8 (10) ◽  
pp. 1790 ◽  
Author(s):  
Xuefeng Li ◽  
Yixian Yang ◽  
Yuling Chen ◽  
Xinxin Niu
Keyword(s):  
Social Networks ◽  
Social Identity ◽  
Online Social Networks ◽  
Measurement Scale ◽  
Sensitive Information ◽  
User Privacy ◽  
Measurement Framework ◽  
Straightforward Method ◽  
Privacy Risks ◽  
Privacy Measurement

Recently, the number of people who are members of multiple online social networks simultaneously has increased. However, if these people share everything with others, they risk their privacy. Users may be unaware of the privacy risks involved with sharing their sensitive information on a network. Currently, there are many research efforts focused on social identity linkage (SIL) on multiple online social networks for commercial services, which exacerbates privacy issues. Many existing studies consider methods of encrypting or deleting sensitive information without considering if this is unreasonable for social networks. Meanwhile, these studies ignore privacy awareness, which is rudimentary and critical. To enhance privacy awareness, we discuss a user privacy exposure measure for users who are members of multiple online social networks. With this measure, users can be aware of the state of their privacy and their position on a privacy measurement scale. Additionally, we propose a straightforward method through our framework to reduce information loss and foster user privacy awareness by using spurious content for required fields.

Controlling privacy disclosure of third party applications in online social networks

2016 ◽  
Vol 12 (2) ◽  
pp. 215-241 ◽  
Author(s):  
Sarath Tomy ◽  
Eric Pardede
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Structural Changes ◽  
Third Party ◽  
Sensitive Information ◽  
User Privacy ◽  
Complete Control ◽  
Content Type ◽  
User Awareness

Purpose The purpose of this paper is to analyse the problem of privacy disclosure of third party applications in online social networks (OSNs) through Facebook, investigate the limitations in the existing models to protect users privacy and propose a permission-based access control (PBAC) model, which gives users complete control over users’ data when accessing third party applications. Design/methodology/approach A practical model based on the defined permission policies is proposed to manage users information accessed by third party applications and improve user awareness in sharing sensitive information with them. This model is a combination of interfaces and internal mechanisms which can be adopted by any OSN having similar architecture to Facebook in managing third party applications, without much structural changes. The model implemented in Web interface connects with Facebook application programming interface and evaluates its efficacy using test cases. Findings The results show that the PBAC model can facilitate user awareness about privacy risks of data passed on to third party applications and allow users who are more concerned about their privacy from releasing such information to those applications. Research limitations/implications The study provides further research in protecting users’ privacy in OSNs and thus avoid the risks associated with that, thereby increasing users’ trust in using OSNs. Originality/value The research has proven to be useful in improving user awareness on the risk associated with sharing private information on OSNs, and the practically implemented PBAC model guarantees full user privacy from unwanted disclosure of personal information to third party applications.

scholarly journals #DontTweetThis: Scoring Private Information in Social Networks

2019 ◽  
Vol 2019 (4) ◽  
pp. 72-92 ◽  
Author(s):  
Qiaozhi Wang ◽  
Hao Xue ◽  
Fengjun Li ◽  
Dongwon Lee ◽  
Bo Luo
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Quantitative Model ◽  
Social Contexts ◽  
Sensitive Information ◽  
Context Aware ◽  
Privacy Leakage ◽  
Privacy Risks ◽  
Context Free

Abstract With the growing popularity of online social networks, a large amount of private or sensitive information has been posted online. In particular, studies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. As such, there exist great needs to be able to identify potentially-sensitive online contents, so that users could be alerted with such findings. In this paper, we propose a context-aware, text-based quantitative model for private information assessment, namely PrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first solicit diverse opinions on the sensitiveness of private information from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to generate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs.

Online Social Networks: Why We Disclose

2010 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Hanna Krasnova ◽  
Sarah Spiekermann ◽  
Ksenia Koroleva ◽  
Thomas Hildebrand
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Information Disclosure ◽  
Structural Equation ◽  
Personal Information ◽  
Equation Model ◽  
Self Disclosure ◽  
Network Provider ◽  
Privacy Calculus ◽  
Privacy Risks

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of Industry players and policymakers worldwide. Despite the Impressive scope of this phenomenon, very little Is understood about what motivates users to disclose personal Information. Integrating focus group results Into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose Information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

Privacy and Security for Virtual Communities and Social Networks

2013 ◽  
pp. 19-31 ◽  
Author(s):  
Georgios Michaelides ◽  
Gábor Hosszú
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Personal Information ◽  
Rapid Development ◽  
Virtual Community ◽  
Privacy And Security ◽  
Internet Privacy ◽  
Large Audience ◽  
Privacy Risks

The importance of the virtual communities’ privacy and security problems comes into prominence by the rapid development of online social networks. This article presents the multiple threats currently plaguing the virtual world, Internet privacy risks, and recommendations and countermeasures to avoid such problems. New generations of users feel comfortable publishing their personal information and narrating their lives. They are often unaware how vulnerable the data in their public profiles are, which a large audience daily accesses. A so-called digital friendship is built among them. Such commercial and social pressures have led to a number of privacy and security risks for social network members. The article presents the most important vulnerabilities and suggests protection methods and solutions that can be utilized according to the threat. Lastly, the authors introduce the concept of a privacy-friendly virtual community site, named CWIW, where privacy methods have been implemented for better user protection.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

Social Identities, Group Formation, and the Analysis of Online Communities

2010 ◽  
pp. 1346-1361 ◽  
Author(s):  
Jillianne R. Code ◽  
Nicholas E. Zaparyniuk
Keyword(s):  
Social Networks ◽  
Social Identity ◽  
Online Communities ◽  
Collective Identity ◽  
Online Social Networks ◽  
Online Social Network ◽  
Group Formation ◽  
Social Identities ◽  
New Members ◽  
The Individual

Central to research in social psychology is the means in which communities form, attract new members, and develop over time. Research has found that the relative anonymity of Internet communication encourages self-expression and facilitates the formation of relationships based on shared values and beliefs. Self-expression in online social networks enables identity experimentation and development. As identities are fluid, situationally contingent, and are the perpetual subject and object of negotiation within the individual, the presented and perceived identity of the individual may not match reality. In this chapter, the authors consider the psychological challenges unique to understanding the dynamics of social identity formation and strategic interaction in online social networks. The psychological development of social identities in online social network interaction is discussed, highlighting how collective identity and self-categorization associates social identity to online group formation. The overall aim of this chapter is to explore how social identity affects the formation and development of online communities, how to analyze the development of these communities, and the implications such social networks have within education.

Social Identities, Group Formation, and the Analysis of Online Communities

2010 ◽  
pp. 86-101 ◽  
Author(s):  
Jillianne R. Code ◽  
Nicholas E. Zaparyniuk
Keyword(s):  
Social Networks ◽  
Social Identity ◽  
Online Communities ◽  
Collective Identity ◽  
Online Social Networks ◽  
Online Social Network ◽  
Group Formation ◽  
Social Identities ◽  
New Members ◽  
The Individual

Central to research in social psychology is the means in which communities form, attract new members, and develop over time. Research has found that the relative anonymity of Internet communication encourages self-expression and facilitates the formation of relationships based on shared values and beliefs. Self-expression in online social networks enables identity experimentation and development. As identities are fluid, situationally contingent, and are the perpetual subject and object of negotiation within the individual, the presented and perceived identity of the individual may not match reality. In this chapter, the authors consider the psychological challenges unique to understanding the dynamics of social identity formation and strategic interaction in online social networks. The psychological development of social identities in online social network interaction is discussed, highlighting how collective identity and self-categorization associates social identity to online group formation. The overall aim of this chapter is to explore how social identity affects the formation and development of online communities, how to analyze the development of these communities, and the implications such social networks have within education.

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