Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1-3 Protecting while Publishing 17-1-3-1 Anonymization <s> Nowadays, partly driven by many Web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. Privacy preserving publishing of social network data becomes a more and more important concern. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the new challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case, and examine the possible problem formulation in three important dimensions: privacy, background knowledge, and data utility. We survey the existing anonymization methods for privacy preservation in two categories: clustering-based approaches and graph modification approaches. <s> BIB001 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1-3 Protecting while Publishing 17-1-3-1 Anonymization <s> Many applications of social networks require identity and/or relationship anonymity due to the sensitive, stigmatizing, or confidential nature of user identities and their behaviors. Recent work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random ids does not guarantee privacy since the identification of the nodes can be seriously jeopardized by applying background based attacks. In this paper, we investigate how well an edge based graph randomization approach can protect node identities and sensitive links. We quantify both identity disclosure and link disclosure when adversaries have one specific type of background knowledge (i.e., knowing the degrees of target individuals). We also conduct empirical comparisons with the recently proposed K-degree anonymization schemes in terms of both utility and risks of privacy disclosures. <s> BIB002 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1-3 Protecting while Publishing 17-1-3-1 Anonymization <s> Anonymization of social networks before they are published or shared has become an important research question. Recent work on anonymizing social networks has looked at privacy preserving techniques for publishing a single instance of the network. However, social networks evolve and a single instance is inadequate for analyzing the evolution of the social network or for performing any longitudinal data analysis. We study the problem of repeatedly publishing social network data as the network evolves, while preserving privacy of users. Publishing multiple instances of the same network independently has privacy risks, since stitching the information together may allow an adversary to identify users in the networks. We propose methods to anonymize a dynamic network such that the privacy of users is preserved when new nodes and edges are added to the published network. These methods make use of link prediction algorithms to model the evolution of the social network. Using this predicted graph to perform group-based anonymization, the loss in privacy caused by new edges can be reduced. We evaluate the privacy loss on publishing multiple social network instances using our methods. <s> BIB003 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 17-1-3 Protecting while Publishing 17-1-3-1 Anonymization <s> In recent years, social networking sites (SNSs) gained high popularity among Internet users as they combine the best of both worlds: befriending people outside real life situations and staying in touch with people already known. An important aspect of any SNS are user profiles, which allow users to virtually publish anything about themselves, including highly personal or sensitive information. With the inception of SNSs, the problem of personal information disclosure and privacy implications has turned into a serious issue. While privacy issues in SNSs have been extensively analyzed in the past five years showcasing flagships of "western" SNSs like Facebook and MySpace, SNSs that target mainly Russian speaking audiences are not yet analyzed and demand investigation. The goals of this paper are twofold: (1) to raise the awareness of the public to the problems of information revelation by studying the amount and type of information disclosed by users of Runet (Russian Segment of the Internet) SNSs (2) to compare our findings to the results of previous studies in the context of "western" SNSs. We investigate different aspects of information revelation of more than 30 million user profiles collected from five Runet SNS considered in this paper. In addition, we conducted a survey among a Russian speaking population to assess both the level of awareness of the privacy issues and the level of trust, and compared the results to previous studies. While the results indicate that Runet users tend to disclose less information and are more concerned about privacy implications, there is still a substantial gap between western and Runet SNS providers in understanding of privacy implications and implementation of security measures, which leads to exposure of extensive amounts of personal information. <s> BIB004
So far we have discussed privacy protection by means of limiting access to users' personal information. In this section we are going to take a look at a kind of protection that aims to protect the privacy and publish information at the same time. As we mentioned, there is a high interest and desire with various motivations to have access to SNSs data. A method that is used to publish this data and protect users' privacy at the same time is called anonymization. An informal definition of anonymization in the context of privacy protection is replacing information that its revelation may damage users' privacy (email, address etc.) with other harmless data. In  and BIB002 the tradeoff between privacy and utility of anonymized data is discussed. A good survey about anonymizing social network information is BIB001 . In this study anonymization methods have been categorized and according to it, the state-of-theart methods for social network information anonymization are clustering based approaches and graph modification approaches. Clustering based approaches include four subcategories of vertex clustering, edge clustering, vertex and edge clustering, and vertex-attribute mapping clustering. Graph modification approaches include three subcategories of optimization, randomized graph modification, and greedy graph modification. In BIB003 a weakness of past studies about anonymization is mentioned, which is their focus on methods that consider a single instance of a network, while SNSs evolutionally change and the information that does not reflect these changes is not enough for every analysis. In this paper some studies that has been done about the evolution of social networks are mentioned and also it is noted that anonymization of different instances taken in different times is not sufficient and comparing them leads to information revelation. Researchers of this paper have cited a report of their own in which they have proposed an approach for this problem. As we will see in the attacks section the beneficiaries still try to acquire their desired information and try to extract it even from anonymized data, so some studies towards improving anonymization techniques and overcoming their weaknesses like [69] have been and will be done. Researchers in BIB004 believe that in the area of users' privacy, mathematical rigor is needed towards having clear definitions about the meaning of comprising privacy and the information that the adversary has access to.