Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes. <s> BIB001 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> Social networking services well know that some users are unwilling to freely share the information they store with the service (e.g. profile information). To address this, ser vices typically provide various privacy "knobs" that the user may adjust to limit access by content type or user identity. However, the main purpose of social networks, community building, is largely at odds with this, hence it is unsurprising that privacy breaches in social networks are increasingly discovered. We argue that this tension between social networking goals and privacy suggests that research efforts should be focused more on efficient methods for detecting privacy breaches in social networks and on building user awareness of privacy risks and the trade-off between privacy and utility. We support our argument with a simple method for discovering LinkedIn contacts ostensibly hidden by privacy settings. This method appears discoverable with a straightforward analysis of the LinkedIn system and its features (indeed, LinkedIn is likely aware of this method), however Linkedin's privacy instructions suggest to users that implementing a privacy setting will prevent such discovery. <s> BIB002 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> Social Networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expression for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mechanism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who promote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been proposed. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today's most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users. <s> BIB003 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> Since their introduction, SNS (Social Networking Sites) such as MySpace, Facebook and LinkedIn have attracted millions of users and have become established places for keeping contact with old acquaintances and meeting new ones. Nonetheless, due to lack of user awareness and proper privacy protection tools, huge quantities of user data, including personal information, pictures and videos are quickly falling into the hands of authorities, strangers, recruiters and even the public at large. By using SNSs and accepting their privacy policy, users have volunteered to relinquish their ownership on their own data, which explains why the proposed privacy solutions based on current SNSs cannot solve all user privacy issues. As such, we start by setting the foundations for privacy and introduce a Privacy Framework for SNSs. Then, based on this framework, we present a User Privacy Policy (UPP) which provides users with an easy and flexible way to specify and communicate their privacy concerns to other users, third parties and to the SNS provider. <s> BIB004 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> 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. <s> BIB005 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> 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. <s> BIB006 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> Now a days, more and more of social network data are being published in one way or other. So, preserving privacy in publishing social network data has become an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Most of the work done so far towards privacy preservation can deal with relational data only. However, Bin Zhou and Jian Pei [11] proposed a scheme for anonymization of social networks, which is an initiative in this direction and provides a partial solution to this problem. In fact, their algorithm cannot handle the situations in which an adversary has knowledge about vertices in the second or higher hops of a vertex, in addition to its immediate neighbors. In this paper, we propose a modification to their algorithm for the network anonymization which can handle such situations. In doing so, we use an algorithm for graph isomorphism based on adjacency matrix instead of their approach using DFS technique [11]. More importantly, the time complexity of our algorithm is less than that of Zhou and Pei. <s> BIB007 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1 Defence 17-1-1 Helping Users <s> Default privacy policies have a significant impact on the overall dynamics and success of online social networks, as users tend to keep their initial privacy policies. In this work-in-progress, we present a new method for suggesting privacy policies for new users by exploring knowledge of existing policies. The defaults generation process performs a collaborative analysis of the policies, finding personalized and representative suggestions. We show how the process can be extended to a wide range of domains, and present results based on 543 privacy policies obtained from a live location-based social network. Finally, we present a user interaction model that lets the user retain control over the default policies, allowing the user to make knowledgeable decisions regarding which default policy to take. <s> BIB008
Users need to be informed about the consequences of their various information publication and their activities; need to know which part of their information is accessible and for whom; need to have some facilities to control the way that people can access their information, and need to get all of these requirements in a simple and understandable way that does not need a lot of time and effort. A user gets acquainted with the matter of privacy from the first steps of her experience on SNSs by facing with privacy policies text. Definitely you too have faced with such a text and confirm that they are not very pleasant for users and a lot of people accept and pass over them without reading. In BIB002 the challenge of showing users' privacy related issues to them in an understandable way is presented with an interesting example where a possible interpretation of a privacy-related text may be different from what really happens. In a study that is done on six well-known SNSs BIB003 , it is mentioned that privacy policies often have internal inconsistencies and also there is a lack of clear phrases about data retention. Overall privacy related texts like privacy policies should be unambiguous, as short as possible, and simply understandable. This can be considered as the first step of helping users towards their privacy protection. After accepting the privacy policy agreement, there are some flexible privacy settings that could be adjusted and help the user to specify the way her information is accessible. Since it is difficult for the average user to adjust these fine grained and detailed settings BIB006 , some research towards helping users to do that in the best way could be useful. In BIB006 a work has been done toward automatically adjusting these settings with the minimum effort of the user. In this study a wizard is introduced that builds a classifier based on the user's answers to the systems requests and uses it to automatically adjust the settings. The possibility of using a limited amount of user's input to build a machine learning model that concisely describes the privacy preferences of a user, and using this model for automatically adjusting privacy settings of a user is presented in this paper. Some other interesting points about this approach are the adoption of the system after a new friend is added by a user (it presents some information about the user's preferences), and the possibility of viewing and modifying the obtained model by advanced users. Another helpful facility (the work is presented by Lipford et al. and is cited in BIB006 ) is a tool that makes the user able to view her own profile from the view point of each of her friends and consequently observing the results of her settings. It seems that some sort of this approach is adopted by facebook BIB006 . In BIB004 a method for suggesting privacy settings to newcomers is presented and the importance of these primitive settings due to the users' tendency towards keeping them is noted. In this study to some extent, a review about how to use machine learning to prepare and suggest primitive privacy settings that are more probably useful for users is presented. Another helpful aid for users' better privacy protection is providing some informative metrics by which users can obtain concise useful information. In BIB002 it is suggested to use an approach which uses data mining and potentially other AI tools to find out the amount of difficulty that accessing users' information has, and to provide some metrics for showing that, that is a useful tool for informing users about their privacy risk. Another metric that is presented in [65] is "privacy score". In this study some models and algorithms have been presented for calculating this metric and some mathematical models are developed to estimate the sensitivity and visibility of the information, which are influential on privacy risk. Sometimes users do not behave appropriately regarding their privacy while using SNSs. Understanding these risky behaviors, their causes, and then informing people and developing preventive mechanisms could be another way of helping users. In BIB001 a few of these risky behaviors are mentioned : incaution in accepting friendship requests, clicking on links received from others without enough caution, reacting to suspicious friendship requests after accepting them (not before) and therefore letting suspicious users to access information, interaction with fake profiles and overall, high implicit trust that exists in SNSs. A study is cited there in which 41% of 200 users whom a friendship request were randomly sent to, accepted it, and most of the users had not limited access to their personal profile information. In another study which is also cited in BIB001 , publicly available information of some people has been gathered from some SNSs, and have been used in phishing emails; results show that targeted people whose received emails contains some information about them or their friends are more likely to get involved. In BIB005 [50] and BIB007 some surveys have been conducted as a way for studying users and acquiring information that leads to better helping them. In BIB005 interviews and surveys are used to investigate the effective factors that cause personal information exposure by students on facebook and why they do it despite the existing concerns. How to defend them against privacy threats is also studied. In BIB007 a survey is conducted in which the participants are highly educated and some information about users' behaviors, viewpoints, and concerns about privacy related issues are gained. Notwithstanding all of the existing threats and risks about private information disclosure, however users extensively tend to engage in social relations and interactions in SNSs and naturally each of these relations and interactions needs some information exposure. In BIB008 a study is done with the goal of determining the least information that needs to be shared to accomplish a specific interaction. An interesting study towards preparing helpful tools for users to protect their privacy is  in which helping users to jointly manage the privacy of their shared content is studied. Sometimes people who benefit from or get harmed by publication of a specific information unit are multiple, and publishing such information may put the privacy of several people at risk. An example of this kind of information that is extensively being published over SNSs is a photo in which several people exist. Photos are sometimes tagged and/or some additional information about them is available besides them. Support for common ownership in SNSs, and the requirements of solutions namely being fair, lightweight, and practical are mentioned as some issues that exist about joint management of privacy in  . In BIB007 it's been tried to find a way for a user to express her privacy preferences, and a method to do this with the aim of being easy to understand by other users and also being machine readable (so that it can be used by other service providers and third parties) is presented. It is mentioned that they intend to use cryptographic techniques to provide the possibility of proving privacy violation for the data owner (for example to a legal authority) which is another helpful facility towards users' privacy protection.