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
2072908603 2092782098 2017922319 2131041789 2045802490 2097427281 2168661288
The task of protecting users' privacy is made more difficult by their attitudes towards information disclosure without full awareness and the economics of the tracking and advertising industry. Even after numerous press reports and widespread disclosure of leakages on the Web and on popular Online Social Networks, many users appear not be fully aware of the fact that their information may be collected, aggregated and linked with ambient information for a variety of purposes. Past attempts at alleviating this problem have addressed individual aspects of the user's data collection. In this paper we move towards a comprehensive and efficient client-side tool that maximizes users' awareness of the extent of their information leakage. We show that such a customizable tool can help users to make informed decisions on controlling their privacy footprint. While Online Social Networks (OSNs) enable users to share photos easily, they also expose users to several privacy threats from both the OSNs and external entities. The current privacy controls on OSNs are far from adequate, resulting in inappropriate flows of information when users fail to understand their privacy settings or OSNs fail to implement policies correctly. OSNs may further complicate privacy expectations when they reserve the right to analyze uploaded photos using automated face identification techniques. In this paper, we propose the design, implementation and evaluation of Cryptagram, a system designed to enhance online photo privacy. Cryptagram enables users to convert photos into encrypted images, which the users upload to OSNs. Users directly manage access control to those photos via shared keys that are independent of OSNs or other third parties. OSNs apply standard image transformations (JPEG compression) to all uploaded images so Cryptagram provides an image encoding and encryption mechanism that is tolerant to these transformations. Cryptagram guarantees that the recipient with the right credentials can completely retrieve the original image from the transformed version of the uploaded encrypted image while the OSN cannot infer the original image. Cryptagram's browser extension integrates seamlessly with preexisting OSNs, including Facebook and Google+, and currently has over 400 active users. Recommender systems predict user preferences based on a range of available information. For systems in which users generate streams of content (e.g., blogs, periodically-updated newsfeeds), users may rate the produced content that they read, and be given accurate predictions about future content they are most likely to prefer. We design a distributed mechanism for predicting user ratings that avoids the disclosure of information to a centralized authority or an untrusted third party: users disclose the rating they give to certain content only to the user that produced this content. We demonstrate how rating prediction in this context can be formulated as a matrix factorization problem. Using this intuition, we propose a distributed gradient descent algorithm for its solution that abides with the above restriction on how information is exchanged between users. We formally analyse the convergence properties of this algorithm, showing that it reduces a weighted root mean square error of the accuracy of predictions. Although our algorithm may be used many different ways, we evaluate it on the Neflix data set and prediction problem as a benchmark. In addition to the improved privacy properties that stem from its distributed nature, our algorithm is competitive with current centralized solutions. Finally, we demonstrate the algorithm's fast convergence in practice by conducting an online experiment with a prototype user-generated content exchange system implemented as a Facebook application. 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. We measure users' attitudes toward interpersonal privacy concerns on Facebook and measure users' strategies for reconciling their concerns with their desire to share content online. To do this, we recruited 260 Facebook users to install a Facebook application that surveyed their privacy concerns, their friend network compositions, the sensitivity of posted content, and their privacy-preserving strategies. By asking participants targeted questions about people randomly selected from their friend network and posts shared on their profiles, we were able to quantify the extent to which users trust their "friends" and the likelihood that their content was being viewed by unintended audiences. We found that while strangers are the most concerning audience, almost 95 of our participants had taken steps to mitigate those concerns. At the same time, we observed that 16.5 of participants had at least one post that they were uncomfortable sharing with a specific friend---someone who likely already had the ability to view it---and that 37 raised more general concerns with sharing their content with friends. We conclude that the current privacy controls allow users to effectively manage the outsider threat, but that they are unsuitable for mitigating concerns over the insider threat---members of the friend network who dynamically become inappropriate audiences based on the context of a post. Traditional theory suggests consumers should be able to manage their privacy. Yet, empirical and theoretical research suggests that consumers often lack enough information to make privacy-sensitive decisions and, even with sufficient information, are likely to trade off long-term privacy for short-term benefits Building on the popularity of online social networks (OSNs) such as Facebook, social content-sharing applications allow users to form communities around shared interests. Millions of users worldwide use them to share recommendations on everything from music and books to resources on the web. However, their increasing popularity is beginning to attract the attention of malicious attackers. As social network credentials become valued targets of phishing attacks and social worms, attackers look to leverage compromised accounts for further financial gain. In this paper, we analyze the state of privacy protection in social content-sharing applications, describe effective privacy attacks against today's social networks, and propose anonymization techniques to protect users. We show that simple protection mechanisms such as anonymizing shared data can still leave users open to social intersection attacks, where a small number of compromised users can identify the originators of shared content. Modeling this as a graph anonymization problem, we propose to provide users with k-anonymity privacy guarantees by augmenting the social graph with "latent edges." We identify StarClique, a locally minimal graph structure required for users to attain k-anonymity, where at worst, a user is identified as one of k possible contributors of a data object. We prove the correctness of our approach using analysis. Finally, using experiments driven by traces from the del.icio.us social bookmark site, we demonstrate the practicality and effectiveness of our approach on real-world systems.