In this paper we provide a preliminary analysis of Google+ privacy. We identied that Google+ shares photo metadata with users who can access the photograph and discuss its potential impact on privacy. We also identied that Google+ encourages the provision of other names including maiden name, which may help criminals performing identity theft. We show that Facebook lists are a superset of Google+ circles, both functionally and logically, even though Google+ provides a better user interface. Finally we compare the use of encryption and depth of privacy control in Google+ versus in Facebook.
Social networks privacy and its potential threats have been widely studied in recent years. One of the earliest works on potential threats to individual's privacy including stalking, embarrassment and identity theft was done by Gross @cite_12 .
@cite_12
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The prevailing paradigm in Internet privacy literature, treating privacy within a context merely of rights and violations, is inadequate for studying the Internet as a social realm. Following Goffman on self-presentation and Altman's theorizing of privacy as an optimization between competing pressures for disclosure and withdrawal, the author investigates the mechanisms used by a sample (n = 704) of college students, the vast majority users of Facebook and Myspace, to negotiate boundaries between public and private. Findings show little to no relationship between online privacy concerns and information disclosure on online social network sites. Students manage unwanted audience concerns by adjusting profile visibility and using nicknames but not by restricting the information within the profile. Mechanisms analogous to boundary regulation in physical space, such as walls, locks, and doors, are favored; little adaptation is made to the Internet's key features of persistence, searchability, and cross-indexa... We consider a privacy threat to a social network in which the goal of an attacker is to obtain knowledge of a significant fraction of the links in the network. We formalize the typical social network interface and the information about links that it provides to its users in terms of lookahead. We consider a particular threat where an attacker subverts user accounts to get information about local neighborhoods in the network and pieces them together in order to get a global picture. We analyze, both experimentally and theoretically, the number of user accounts an attacker would need to subvert for a successful attack, as a function of his strategy for choosing users whose accounts to subvert and a function of lookahead provided by the network. We conclude that such an attack is feasible in practice, and thus any social network that wishes to protect the link privacy of its users should take great care in choosing the lookahead of its interface, limiting it to 1 or 2, whenever possible. Despite concerns raised about the disclosure of personal information on social network sites, research has demonstrated that users continue to disclose personal information. The present study employs surveys and interviews to examine the factors that influence university students to disclose personal information on Facebook. Moreover, we study the strategies students have developed to protect themselves against privacy threats. The results show that personal network size was positively associated with information revelation, no association was found between concern about unwanted audiences and information revelation and finally, students' Internet privacy concerns and information revelation were negatively associated. The privacy protection strategies employed most often were the exclusion of personal information, the use of private email messages, and altering the default privacy settings. Based on our findings, we propose a model of information revelation and draw conclusions for theories of identity expression. A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities. In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.