Privacy is an enormous problem in online social networking sites. While sites such as Facebook allow users fine-grained control over who can see their profiles, it is difficult for average users to specify this kind of detailed policy. In this paper, we propose a template for the design of a social networking privacy wizard. The intuition for the design comes from the observation that real users conceive their privacy preferences (which friends should be able to see which information) based on an implicit set of rules. Thus, with a limited amount of user input, it is usually possible to build a machine learning model that concisely describes a particular user’s preferences, and then use this model to configure the user’s privacy settings automatically. As an instance of this general framework, we have built a wizard based on an active learning paradigm called uncertainty sampling. The wizard iteratively asks the user to assign privacy “labels ” to selected (“informative”) friends, and it uses this input to construct a classifier, which can in turn be used to automatically assign privileges to the rest of the user’s (unlabeled) friends. To evaluate our approach, we collected detailed privacy preference data from 45 real Facebook users. Our study revealed two important things. First, real users tend to conceive their privacy preferences in terms of communities, which can easily be extracted from a social network graph using existing techniques. Second, our active learning wizard, using communities as features, is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools.

The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy. In this paper, we focus on measuring the disparity between the desired and actual privacy settings, quantifying the magnitude of the problem of managing privacy. We deploy a survey, implemented as a Facebook application, to 200 Facebook users recruited via Amazon Mechanical Turk. We find that 36 % of content remains shared with the default privacy settings. We also find that, overall, privacy settings match users ’ expectations only 37 % of the time, and when incorrect, almost always expose content to more users than expected. Finally, we explore how our results have potential to assist users in selecting appropriate privacy settings by examining the user-created friend lists. We find that these have significant correlation with the social network, suggesting that information from the social network may be helpful in implementing new tools for managing privacy.

The management of group context in socially mediating technologies is an important challenge for the design community. To better understand how users manage group context, we explored the practice of multiple profile management in social media. In doing so, we observed creative and opportunistic strategies for group context management. We found that multiple profile maintenance is motivated by four factors: privacy, identity, utility, and propriety. Drawing on these motives, we observe a continuum of boundary regulation behaviors: pseudonymity, practical obscurity, and transparent separation. Based on these findings, we encourage designers of group context management systems to more broadly consider motives and practices of group separations in social media. Group context management systems should be privacy-enhancing, but a singular focus on privacy overlooks a range of other group context management practices. Author Keywords Privacy, social media, qualitative research

We hypothesize that, in a Facebook-style social network system, proper visualization of one’s extended neighborhood could help the user understand the privacy implications of her access control policies. However, an unrestricted view of one’s extended neighborhood may compromise the privacy of others. To address this dilemma, we propose a privacy-enhanced visualization tool, which approximates the extended neighborhood of a user in such a way that policy assessment can still be conducted in a meaningful manner, while the privacy of other users is preserved. 1