Facebook: public space, or private space?

Social networks have become a central feature of everyday life. Most young people are members of at least one online social network, and they naturally provide a great deal of personal information as a condition for participation in the rich online social lives these networks afford. Increasingly, this information is being used as evidence in criminal and even civil legal proceedings. These latter uses, by actors involved in the justice system, are typically justified on the grounds that social network information is essentially public in nature, and thus does not generate a subjective expectation of privacy necessary to support a civil rights-based privacy protection. This justification, however, is based on the perceptions of individuals who are outside the online social network community, rather than reflecting the norms and privacy practices of participants in online social networks. This project takes a user-centric approach to the question of whether online social spaces are public venues, examining of the information-related practices of social network participants, focusing on how they treat their own information and that of others posted in online social spaces. Our results reveal that online social spaces are indeed loci of public display rather than private revelation: online profiles are structured with the view that ‘everyone’ can see them, even if the explicitly intended audience is more limited. These social norms are inconsistent with the claim that social media are private spaces; instead, it appears that participants view and treat online social networks as public venues

Securing Heterogeneous Privacy Protection in Social Network Records based Encryption Scheme

This survey places of interest the major issues concerning privacy and security in online social networks. Firstly, we discuss investigate that aims to protect user data from the an assortment of attack vantage points together with other users, advertisers, third party request developers, and the online social arrangement provider itself. Next we cover social network supposition of user attributes, locate hubs, and link prediction. Because online social networks are so saturated with sensitive information, network inference plays a major privacy role. Social Networking sites go upwards since of all these reasons. In recent years indicates that for many people they are now the mainstream communication knowledge. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems threats and attacks such as revelation of information, identity thefts etc. Privacy practice in social networking sites often appear convoluted as in sequence sharing stands in discord with the need to reduce disclosure-related abuses. Facebook is one such most popular and widely used Social Networking sites which have its own healthy set of Privacy policy

It Won\u27t Happen To Me! : Self-Disclosure in Online Social Networks

Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions

PRIVACY ISSUES IN ONLINE SOCIAL NETWORKS: USER BEHAVIORS AND THIRD-PARTY APPLICATIONS

In contemporary society, social networking websites has developed dramatically and became an indispensable component in our daily life. Since it can help create a more feature-rich online social community, third-party service has been widely adopted in online social networks (OSNs). Integrating these third-party sites and applications has not only extended business of both social network server and third party and but also promises to break down the garden walls of social-networking sites. While at the same time it dramatically raises concerns on privacy leakage. This article mainly focuses on the privacy disclosure issues caused by user’s behavior and third-party applications and websites. On the one hand, because of the diversity of usage behaviors, the revelation of personal information varies significantly. A survey is conducted to present empirical and quantitative result. On the other hand, the access mechanism between OSN and third party is not perfect enough. Besides, it could be a potential source of privacy leak that third-party services sometimes act as advertisers and information aggregators of a user\u27s traversals. The relevant reasons and internal and external threats are presented. Finally, possible solutions to reduce the increasing information disclosure are provided. Actions should be taken along three fronts: the government, the users themselves as well as the third parties

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

BFF: A tool for eliciting tie strength and user communities in social networking services

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10796-013-9453-6The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and TIN 2008-04446 and PROMETEO II/2013/019 projects. 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Analyzing users’ behaviour to identify their privacy concerns

The majority of studies examining privacy concerns of Internet users are based on surveys. Many problems have, however, been identified with using surveys to measure people’s privacy concerns. Based on our experience from our previous studies, in this paper we discuss how ethnographic interviews and observation techniques could be used to analyze users’ behaviour in terms of how they share personal information and multimedia content with others, and utilize this to identify issues related to their privacy concerns more comprehensively than it is otherwise possible with conventional surveys

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin