The problem of online privacy is often reduced to individual decisions to hide or reveal personal information in online social networks (OSNs). However, with the increasing use of OSNs, it becomes more important to understand the role of the social network in disclosing personal information that a user has not revealed voluntarily: How much of our private information do our friends disclose about us, and how much of our privacy is lost simply because of online social interaction? Without strong technical effort, an OSN may be able to exploit the assortativity of human private features, this way constructing shadow profiles with information that users chose not to share. Furthermore, because many users share their phone and email contact lists, this allows an OSN to create full shadow profiles for people who do not even have an account for this OSN. We empirically test the feasibility of constructing shadow profiles of sexual orientation for users and non-users, using data from more than 3 Million accounts of a single OSN. We quantify a lower bound for the predictive power derived from the social network of a user, to demonstrate how the predictability of sexual orientation increases with the size of this network and the tendency to share personal information. This allows us to define a privacy leak factor that links individual privacy loss with the decision of other individuals to disclose information. Our statistical analysis reveals that some individuals are at a higher risk of privacy loss, as prediction accuracy increases for users with a larger and more homogeneous first- and second-order neighborhood of their social network. While we do not provide evidence that shadow profiles exist at all, our results show that disclosing of private information is not restricted to an individual choice, but becomes a collective decision that has implications for policy and privacy regulation.
Understanding privacy in OSNs starts with the individual motivation to share personal information and its associated risk of sharing this information with undesired contacts @cite_45 @cite_40 . Most OSNs include highly customizable modules to control privacy settings, which can lead to higher efforts and uncertainty how to use the site @cite_37 , or to distancing from those users that have a lower awareness of possible data leakage @cite_4 @cite_11 @cite_15 . Recent technologies promise to alleviate user privacy concerns. For example, distributed recommender systems can put a limit to privacy disclosure @cite_42 , deployment of OSNs in the cloud can avoid the centralization of user data @cite_3 @cite_8 , techniques for picture encryption @cite_33 and content anonymization @cite_16 can prevent undesired access to private content.
@cite_37 @cite_4 @cite_33 @cite_8 @cite_42 @cite_3 @cite_40 @cite_45 @cite_15 @cite_16 @cite_11
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We have seen tremendous growth in online social networks (OSNs) in recent years. These OSNs not only offer attractive means for virtual social interactions and information sharing, but also raise a number of security and privacy issues. Although OSNs allow a single user to govern access to her his data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users, remaining privacy violations largely unresolved and leading to the potential disclosure of information that at least one user intended to keep private. In this paper, we propose an approach to enable collaborative privacy management of shared data in OSNs. In particular, we provide a systematic mechanism to identify and resolve privacy conflicts for collaborative data sharing. Our conflict resolution indicates a tradeoff between privacy protection and data sharing by quantifying privacy risk and sharing loss. We also discuss a proof-of-concept prototype implementation of our approach as part of an application in Facebook and provide system evaluation and usability study of our methodology. While highly successful, today's online social networks (OSNs) have made a conscious decision to sacrifice privacy for availability and centralized control. Unfortunately, tradeoffs in this "walled garden" architecture naturally pit the economic interests of OSN providers against the privacy goals of OSN users, a battle that users cannot win. While some alternative OSN designs preserve user control over data, they do so by de-prioritizing issues of economic incentives and sustainability. In contrast, we believe any practical alternative to today's centralized architecture must consider incentives for providers as a key goal. In this paper, we propose a distributed OSN architecture that significantly improves user privacy while preserving economic incentives for OSN providers. We do so by using a standardized API to create a competitive provider marketplace for different components of the OSN, thus allowing users to perform their own tradeoffs between cost, performance, and privacy. We describe Polaris, a system where users leverage smartphones as a highly available identity provider and access control manager, and use application prototypes to show how it allows data monetization while limiting the visibility of any single party to users' private data. Due to the amount of personally identiable information shared by users of online social networks (OSNs) and the often not adequately adjusted privacy settings, it is possible to identify a user’s several OSN proles. In this paper, we illustrate that third parties just have to take a small step to link OSN proles of a user and, consequently, to aggregate various pieces of information shared in several OSNs. Particularly, based on statistical results we illustrate how the often publicly available friends lists can be exploited to link several OSN proles of a single natural person. The results presented in this paper show that proles can be linked even at low cost, i.e., without complex correlation techniques or high computational power. To assess the risk of privacy leakage by prole linking, we, additionally, report how often specic pieces of information are made publicly available by users in four of the most popular OSNs. We show that users tend to publish dierent pieces of information in dierent OSNs and, thereby, demonstrate that by linking friends lists more information about a user can be gained than the user shared in a single OSN. For the study we analyzed more than 180,000 user proles and compared more than seven million pairs of proles to investigate prole linkability. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers.