Extent of private information disclosure on online social networks: An exploration of Facebook mobile phone users

2013 ◽  
Vol 29 (6) ◽  
pp. 2722-2729 ◽  
Author(s):  
Victoria Kisekka ◽  
Sharmistha Bagchi-Sen ◽  
H. Raghav Rao
Keyword(s):  
Social Networks ◽  
Mobile Phone ◽  
Private Information ◽  
Online Social Networks ◽  
Information Disclosure

scholarly journals Location-sharing protocol for privacy protection in mobile online social networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ou Ruan ◽  
Lixiao Zhang ◽  
Yuanyuan Zhang
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Smart Cities ◽  
Location Based Services ◽  
Broadcast Encryption ◽  
Location Sharing ◽  
Identity Privacy ◽  
And Performance ◽  
Network Server

AbstractLocation-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

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.

Managing the virtual boundaries: Online social networks, disclosure, and privacy behaviors

2016 ◽  
Vol 20 (1) ◽  
pp. 50-67 ◽  
Author(s):  
Mary Helen Millham ◽  
David Atkin
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Privacy Management ◽  
Communication Privacy Management ◽  
Communication Privacy Management Theory ◽  
Online Setting ◽  
Communication Privacy ◽  
Privacy Behaviors

Online social networks are designed to encourage disclosure while also having the ability to disrupt existing privacy boundaries. This study assesses those individuals who are the most active online: “Digital Natives.” The specific focus includes participants’ privacy beliefs; how valuable they believe their personal, private information to be; and what risks they perceive in terms of disclosing this information in a fairly anonymous online setting. A model incorporating these concepts was tested in the context of communication privacy management theory. Study findings suggest that attitudinal measures were stronger predictors of privacy behaviors than were social locators. In particular, support was found for a model positing that if an individual placed a higher premium on their personal, private information, they would then be less inclined to disclose such information while visiting online social networking sites.

scholarly journals Responsible Research on Social Networks

2018 ◽  
pp. 558-576
Author(s):  
Jon Crowcroft ◽  
Hamed Haddadi ◽  
Tristan Henderson
Keyword(s):  
Social Networks ◽  
Interpersonal Communication ◽  
Private Information ◽  
Online Social Networks ◽  
Personal Data ◽  
Legal Requirements ◽  
Pros And Cons ◽  
Conducting Research ◽  
Responsible Research ◽  
Responsible Manner

Researchers have found online social networks a goldmine for research into various aspects of social behavior and interpersonal communication. For example, observing social interaction between individuals and their engagement in conversations, or performing sentiment analysis on these communications, is often carried out for research in a number of disciplines such as health, sociology, or politics. Such studies introduce many challenges for conducting research in a responsible manner. Data may be repurposed or cross-correlated in ways that participants may not have anticipated or desired, private information may be collected, or legal requirements may not be met. This chapter explores some of the challenges and dilemmas faced by industry, academia, regulators, privacy advocates, and ultimately the individuals using these services. It discusses the pros and cons of the collection, analysis, and archiving of personal data for digital research. The chapter concludes by discussing theoretical and practical approaches that target these dilemmas.

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.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2021 ◽  
pp. 2110-2134
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.

scholarly journals Fear of Missing Out, online social networking and mobile phone addiction: A latent profile approach

2017 ◽  
Vol 35 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Héctor Fuster ◽  
Ander Chamarro ◽  
Ursula Oberst
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Mobile Phone ◽  
Online Social Networks ◽  
Online Survey ◽  
Latent Profile Analysis ◽  
Fear Of Missing Out ◽  
Mobile Phone Addiction ◽  
High Engagement ◽  
Latent Profile

Background and aims: Fear of missing out (FoMO) is described as a pervasive unpleasant sensation that others might be having rewarding experiences of which one is not part, as well as the desire to stay continually connected with what others are doing. It has shown to play an important mediating role in predicting negative outcomes of heavy use of these networks. The aim of the present study was to analyze the different profiles found among users. Methods: 5,280 Spanish speaking social media users from Latin America replied in an online survey to the Spanish version of the FoMO scale, to a short set of questionnaires on online social network use (frequency, intensity and type of access) and indicators of mobile phone addiction. Results: FoMO correlated with the number of different networks used and with all indicators of social network use and mobile phone addiction. Using a Latent Profile Analysis, four classes of users were identified: low-engagement light users, high-engagement heavy users, high-engagement low-risk users, and high-engagement high-risk users; individuals from the fourth class can be considered at risk for developing addiction to online social networks (7.6 % of the sample). Discussion: Accessing the social networks via the mobile phone and presenting addictive behavior seem to be important correlates of FoMO. 

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