Consequences of Publishing Real Personal Information in Online Social Networks

Author(s):  
Theodoros Tzouramanis ◽  
Eleni Vourou ◽  
Argyro Gkorogia
2010 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Hanna Krasnova ◽  
Sarah Spiekermann ◽  
Ksenia Koroleva ◽  
Thomas Hildebrand

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.


Author(s):  
Georgios Michaelides ◽  
Gábor Hosszú

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.


2018 ◽  
Vol 7 (1.7) ◽  
pp. 142
Author(s):  
Hemalatha D ◽  
Almas Begum ◽  
Alex David S

Presently, the growth of Social media is explosive among the users. Increasingly developed social websites like Flickr, Facebook, Google+, LinkedIn etc permits the users to create, share and view the post. Confidentiality is a leading factor required in Social Networks. The social users upload their photos to the social sites that intend to gain public interest for social purposes. The exposure of personal information leads to slipping process like identity stealing, morphing etc, which are against the privacy violations. Relied upon the personal characteristics of users, the privacy settings of each user should be defined. In this paper, a relational study about the privacy settings in Online Social structure is examined. Initiated by the importance of social networks among the social users and their behavior towards Online Social Networks, which is followed by the privacy techniques suggested by other researchers are explored. At last, an overview about the merits and demerits of privacy designs and schemes for the user-uploaded images are presented. The study results a new privacy system that controls the confidential information from being accessed from different devices, including mobile devices and computers.


Author(s):  
Luca Caviglione ◽  
Mauro Coccoli ◽  
Alessio Merlo

With millions of users, Online Social Networks (OSNs) are a huge cultural phenomenon. Put briefly, they are characterized by: i) an intrinsic sharing of personal information, ii) a rich set of features to publish, organize and retrieve contents, especially for emphasizing their social organization, iii) the interaction with a heterogeneous set of devices, e.g., ranging from desktops to mobile appliances, and iv) the mix of Web-based paradigms and sophisticated methodologies for processing data. However, if not properly implemented, or without effective security policies, i) – iv) could lead to severe risks in terms both of privacy and security. In this perspective, this chapter analyzes the major peculiarities of OSN platforms, the preferred development methodologies, and usage patterns, also by taking into account how personal information can be exploited to conduct malicious actions. Then, a graph-based modeling approach is introduced to reveal possible attacks, as well as to elaborate the needed countermeasures or (automated) checking procedures.


Author(s):  
Sanjeev Rao ◽  
Anil Kumar Verma ◽  
Tarunpreet Bhatia

Online social networks (OSNs) are renowned powerful web tools that allow worldwide users to connect with their friends, families, professional groups, and social circle through social interaction for sharing common interests. With the proliferation, ease, and efficacy of OSNs, these are becoming an essential tool for communication. But many OSN users innocently uncover their personal information such as their home address, phone numbers, email id, etc. that can be used by the malicious user to perform various kind for cyber-crimes like cyber-bullying, spamming, click-jacking, identity theft, phishing, distrust, fake profiles, spreading malicious content, etc. This chapter presents a review of various privacy and security threats/attacks associated with OSN users and recommended combating techniques based on data-mining and machine learning algorithms. Also, the future directions for upcoming researchers in this field are suggested.


2011 ◽  
Vol 15 (3) ◽  
pp. 13-19 ◽  
Author(s):  
Danesh Irani ◽  
Steve Webb ◽  
Kang Li ◽  
Calton Pu

2016 ◽  
Vol 20 (3) ◽  
pp. 845-861 ◽  
Author(s):  
Alexandre Fortier ◽  
Jacquelyn Burkell

Earlier research using qualitative techniques suggests that the default conception of online social networks is as public spaces with little or no expectation of control over content or distribution of profile information. Some research, however, suggests that users within these spaces have different perspectives on information control and distribution. This study uses Q methodology to investigate subjective perspectives with respect to privacy of, and control over, Facebook profiles. The results suggests three different types of social media users: those who view profiles as spaces for controlled social display, exerting control over content or audience; those who treat their profiles as spaces for open social display, exercising little control over either content or audience; and those who view profiles as places to post personal information to a controlled audience. We argue that these different perspectives lead to different privacy needs and expectations.


2020 ◽  
Vol 10 (14) ◽  
pp. 4835
Author(s):  
Cheng-Te Li ◽  
Zi-Yun Zeng

Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social network analysis (SNA) tasks, it is essential to learn effective node embeddings from privacy-protected sparse and incomplete network data. In this work, we present a novel NRL model to generate node embeddings that can afford data incompleteness coming from user privacy protection. We propose a structure-attribute enhanced matrix (SAEM) to alleviate data sparsity and develop a community-cluster informed NRL method, c2n2v, to further improve the quality of embedding learning. Experiments conducted across three datasets, three simulations of user privacy protection, and three downstream SNA tasks exhibit the promising performance of our NRL model SAEM+c2n2v.


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