A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localize... <s> BIB001 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Since privacy information can be inferred via social relations, the privacy confidentiality problem becomes increasingly challenging as online social network services are more popular. Using a Bayesian network approach to model the causal relations among people in social networks, we study the impact of prior probability, influence strength, and society openness to the inference accuracy on a real online social network. Our experimental results reveal that personal attributes can be inferred with high accuracy especially when people are connected with strong relationships. Further, even in a society where most people hide their attributes, it is still possible to infer privacy information. <s> BIB002 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Measuring privacy risk in online social networks is a challenging task. One of the fundamental difficulties is quantifying the amount of information revealed unintentionally. We present PrivAware, a tool to detect and report unintended information loss in online social networks. Our goal is to provide a rudimentary framework to identify privacy risk and provide solutions to reduce information loss. The first instance of the software is focused on information loss attributed to social circles. In subsequent releases we intend to incorporate additional capabilities to capture ancillary threat models. From our initial results, we quantify the privacy risk attributed to friend relationships in Facebook. We show that for each user in our study a majority of their personal attributes can be derived from social contacts. Moreover, we present results denoting the number of friends contributing to a correctly inferred attribute. We also provide similar results for different demographics of users. The intent of PrivAware is to not only report information loss but to recommend user actions to mitigate privacy risk. The actions provide users with the steps necessary to improve their overall privacy measurement. One obvious, but not ideal, solution is to remove risky friends. Another approach is to group risky friends and apply access controls to the group to limit visibility. In summary, our goal is to provide a unique tool to quantify information loss and provide features to reduce privacy risk. <s> BIB003 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> On-line social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed private information. In this paper, we explore how to launch inference attacks using released social networking data to predict undisclosed private information about individuals. We then explore the effectiveness of possible sanitization techniques that can be used to combat such inference attacks under different scenarios. <s> BIB004 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information (which is often not hidden) to infer sensitive attributes. The key novel idea is that in addition to friendship links, groups can be carriers of significant information. We show that on several well-known social media sites, we can easily and accurately recover the information of private-profile users. To the best of our knowledge, this is the first work that uses link-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles. <s> BIB005 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Public information about one's coworkers, friends, family, and acquaintances, as well as one's associations with them, implicitly reveals private information. Social-networking websites, e-mail, instant messaging, telephone, and VoIP are all technologies steeped in network data—data relating one person to another. Network data shifts the locus of information control away from individuals, as the individual's traditional and absolute discretion is replaced by that of his social-network. Our research demonstrates a method for accurately predicting the sexual orientation of Facebook users by analyzing friendship associations. After analyzing 4,080 Facebook profiles from the MIT network, we determined that the percentage of a given user's friends who self-identify as gay male is strongly correlated with the sexual orientation of that user, and we developed a logistic regression classifier with strong predictive power. Although we studied Facebook friendship ties, network data is pervasive in the broader context of computer-mediated communication, raising significant privacy issues for communication technologies to which there are no neat solutions. <s> BIB006 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes. In this paper, we ask the question: given attributes for some fraction of the users in an online social network, can we infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To answer this question, we gather fine-grained data from two social networks and try to infer user profile attributes. We find that users with common attributes are more likely to be friends and often form dense communities, and we propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users. <s> BIB007 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> As the popularity of social networks expands, the information users expose to the public has potentially dangerous implications for individual privacy. While social networks allow users to restrict access to their personal data, there is currently no mechanism to enforce privacy concerns over content uploaded by other users. As group photos and stories are shared by friends and family, personal privacy goes beyond the discretion of what a user uploads about himself and becomes an issue of what every network participant reveals. In this paper, we examine how the lack of joint privacy controls over content can inadvertently reveal sensitive information about a user including preferences, relationships, conversations, and photos. Specifically, we analyze Facebook to identify scenarios where conflicting privacy settings between friends will reveal information that at least one user intended remain private. By aggregating the information exposed in this manner, we demonstrate how a user's private attributes can be inferred from simply being listed as a friend or mentioned in a story. To mitigate this threat, we show how Facebook's privacy model can be adapted to enforce multi-party privacy. We present a proof of concept application built into Facebook that automatically ensures mutually acceptable privacy restrictions are enforced on group content. <s> BIB008 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Little research examines the privacy threats associated with the use of third-party apps on Facebook. To address this gap in the literature, we systematically study third-party apps' current practices for privacy notice and consent by: i) collecting data from the 1800 most popular Facebook apps to record their data collection practices concerning users and their friends, and ii) developing our own Facebook app to conduct a number of tests to identify problems that exist in the current design of authentication dialogs for third-party apps on Facebook. To address these problems, we propose two new interface designs for third-party apps' authentication dialogs to: i) increase user control of apps' data access and restrict apps' publishing ability during the process of adding them to users' profiles, and ii) alert users when their global privacy settings on Facebook are violated by apps. This research provides both conceptual and empirical insights in terms of design recommendations to address privacy concerns toward third-party apps on Facebook. <s> BIB009 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Online Social Networks have gained increased popularity in recent years. However, besides their many advantages, they also represent privacy risks for the users. In order to control access to their private information, users of OSNs are typically allowed to set the visibility of their profile attributes, but this may not be sufficient, because visible attributes, friendship relationships, and group memberships can be used to infer private information. In this paper, we propose a fully automated approach based on machine learning for inferring undisclosed attributes of OSN users. Our method can be used for both classification and regression tasks, and it makes large scale privacy attacks feasible. We also provide experimental results showing that our method achieves good performance in practice. <s> BIB010 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user. <s> BIB011 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public. To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service. <s> BIB012 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Lawmakers, children's advocacy groups and modern society at large recognize the importance of protecting the Internet privacy of minors (under 18 years of age). Online Social Networks, in particular, take precautions to prevent third parties from using their services to discover and profile minors. These precautions include displaying only minimal information in registered minors' public profiles, not listing minors when searching for users by high school or city, and banning young children from joining altogether. In this paper we show how an attacker can circumvent these precautions. We develop efficient crawling and data mining methodologies to discover and profile most of the high school students in a targeted high school. In particular, using Facebook and for a given target high school, the methodology finds most of the students in the school, and for each discovered student infers a profile that includes significantly more information than is available in a registered minor's public profile. Such profiles can be used for many nefarious purposes, including selling the profiles to data brokers, large-scale automated spear-phishing attacks on minors, as well as physical safety attacks such as stalking, kidnapping and arranging meetings for sexual abuse. Ironically, the Children's Online Privacy Protection Act (COPPA), a law designed to protect the privacy of children, indirectly facilitates the approach. In order to bypass restrictions put in place due to the COPPA law, some children lie about their ages when registering, which not only increases the exposure for themselves but also for their non-lying friends. Our analysis strongly suggests there would be significantly less privacy leakage if Facebook did not have age restrictions. <s> BIB013 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> Users share massive amounts of personal information and opinion with each other and different service providers every day. In such an interconnected setting, the privacy of individual users is bound to be affected by the decisions of others, giving rise to the phenomenon which we term as interdependent privacy. In this paper we define online privacy interdependence, show its existence through a study of Facebook application permissions, and model its impact through an Interdependent Privacy Game (IPG). We show that the arising negative externalities can steer the system into equilibria which are inefficient for both users and platform vendor. We also discuss how the underlying incentive misalignment, the absence of risk signals and low user awareness contribute to unfavorable outcomes. <s> BIB014 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> In this paper, we introduce a navigation privacy attack, where an external adversary attempts to find a target user by exploiting publicly visible attributes of intermediate users. If such an attack is successful, it implies that a user cannot hide simply by excluding himself from a central directory or search function. The attack exploits the fact that most attributes (such as place of residence, age, or alma mater) tend to correlate with social proximity, which can be exploited as navigational cues while crawling the network. The problem is exacerbated by privacy policies where a user who keeps his profile private remains nevertheless visible in his friends’ “friend lists”; such a user is still vulnerable to our navigation attack. Experiments with Facebook and Google+ show that the majority of users can be found efficiently using our attack, if a small set of attributes are known about the target as side information. Our results suggest that, in an online social network where many users reveal a (even limited) set of attributes, it is nearly impossible for a specific user to “hide in the crowd”. <s> BIB015 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> 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. <s> BIB016 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we call Social-Attribute Network (SAN), to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with a restart algorithm to the SAN framework and showed improved performance. In this article, we extend the SAN framework with several leading supervised and unsupervised link-prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, large-scale Googlep dataset, which we make publicly available (rlhttp://www.cs.berkeley.edu/∼stevgong/gplus.html). <s> BIB017 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> In the attribute inference problem, we aim to infer users' private attributes (e.g., locations, sexual orientation, and interests) using their public data in online social networks. State-of-the-art methods leverage a user's both public friends and public behaviors (e.g., page likes on Facebook, apps that the user reviewed on Google Play) to infer the user's private attributes. However, these methods suffer from two key limitations: 1) suppose we aim to infer a certain attribute for a target user using a training dataset, they only leverage the labeled users who have the attribute, while ignoring the label information of users who do not have the attribute; 2) they are inefficient because they infer attributes for target users one by one. As a result, they have limited accuracies and applicability in real-world social networks. ::: ::: In this work, we propose AttriInfer, a new method to infer user attributes in online social networks. AttriInfer can leverage both friends and behaviors, as well as the label information of training users who have an attribute and who do not have the attribute. Specifically, we model a social network as a pairwise Markov Random Field (pMRF). Given a training dataset, which consists of some users who have a certain attribute and some users who do not have a certain attribute, we compute the posterior probability that a target user has the attribute and use the posterior probability to infer attributes. In the basic version of AttriInfer, we use Loopy Belief Propagation (LBP) to compute the posterior probability. However, LBP is not scalable to very large-scale real-world social networks and not guaranteed to converge. Therefore, we further optimize LBP to be scalable and guaranteed to converge. We evaluated our method and compare it with state-of-the-art methods using a real-world Google+ dataset with 5.7M users. Our results demonstrate that our method substantially outperforms state-of-the-art methods in terms of both accuracy and efficiency. <s> BIB018 </s> A Survey on Interdependent Privacy <s> Demographics and Preferences Data <s> We investigate whether personal information posted by job candidates on social media sites is sought and used by prospective employers. We create profiles for job candidates on popular social networks, manipulating information protected under U.S. laws, and submit job applications on their behalf to over 4,000 employers. We find evidence of employers searching online for the candidates. After comparing interview invitations for a Muslim versus a Christian candidate, and a gay versus a straight candidate, we find no difference in callback rates for the gay candidate compared to the straight candidate, but a 13% lower callback rate for the Muslim candidate compared to the Christian candidate. While the difference is not significant at the national level, it exhibits significant and robust heterogeneity in bias at the local level, compatible with existing theories of discrimination. In particular, employers in Republican areas exhibit significant bias both against the Muslim candidate, and in favor of the Christian candidate. This bias is significantly larger than the bias in Democratic areas. The results are robust to using state- and county-level data, to controlling for firm, job, and geographical characteristics, and to several model specifications. The results suggest that 1) the online disclosure of certain personal traits can influence the hiring decisions of U.S. firms and 2) the likelihood of hiring discrimination via online searches varies across employers. The findings also highlight the surprisingly lasting behavioral influence of traditional, offline networks in processes and scenarios where online interactions are becoming increasingly common. <s> BIB019
Context. Studies on real-life social networks have demonstrated the tendency of individuals to bond with individuals who share certain characteristics with them. Such characteristics include gender, age, social class, geographic location, and preferences. This phenomenon is known as homophily (or through the saying, "birds of a feather flock together"), a term coined by BIB001 and is well documented in the sociology community. Naturally, homophily introduces a correlation between the personal data of individuals bound by social ties. This correlation can be exploited for inference purposes, thus creating interdependent privacy risks. Such risks have materialized on a large scale with the proliferation of OSNs (e.g., FB and Twitter) where individuals disclose their demographic attributes and preferences (characteristics known to be covered by homophily), as well as social ties (e.g., friends, colleagues). The disclosure of such social network data-known as user profiles-is often times made in a structured way, through specific data fields for which the values must be selected in predefined lists, thus facilitating the algorithmic processing of the data. Furthermore, social network data can be made available to various entities beyond the OSN operators themselves, including other users of the OSNs, third parties (e.g., developers) or even the public. Beyond the aforementioned privacy risks based on homophily, the simple fact that users reveal information to their friends (e.g., on OSNs) creates new privacy risks, because their friends can give away this information to other entities, including other users (e.g., their own friends) and third parties (e.g., developers of apps for OSNs). In fact, Facebook's data-use policy contains a paragraph named "I. What kinds of information do we collect?→Things you and others do and provide→Things others do and information they provide about you," 13 which states: "We also receive and analyze content, communications and information that other people provide when they use our Products. This can include information about you, such as when others share or comment on a photo of you, send a message to you, or upload, sync or import your contact information." And Facebook aggregates the information provided by multiple users about some individual in the form of a so-called shadow profile (even for non-Facebook users). 14 OSN data has been proven to be used as a basis for discrimination by (potential) employers (e.g., BIB019 ), identity theft, and cyberstalking, hence it is quite sensitive. Risks. He et al. were the first to identify and formalize the interdependent privacy risks related to OSN data where personal information about a user 15 can be inferred from the data of their contacts (often referred to as "friends") BIB002 ]. They formalize the problem and provide the technical foundations, based on Bayesian networks (see Section 3.1), for assessing privacy risks in such situations. In particular, they consider the cases (1) where the information of only the target's friends is used and (2) where the information about the friends of the target's friends (and so on) is used. They evaluate the performance of their inference techniques-hence the magnitude of the associated privacy risks-by using a dataset of N ≈ 67k users of the LiveJournal online weblog with real ties and synthetic attribute values. Their results show a substantial increase in accuracy (from 70% to up to 95%) when information about (friends of) friends is used. Becker and Chen focus on generic attribute inference based on the attributes of users' friends BIB003 ]. More specifically, users are assigned a given value for one of their attributes if at least a certain number of their friends have this value for the considered attribute. The authors focus their work on FB and consider 12 different attributes including age, relationship status, and zip code. They propose a tool named PrivAware to enable users to assess and to reduce the accuracy of such an inference. Lindamood et al. focus on (binary) political orientation (conservative vs. liberals) inference BIB004 ]. They evaluate the performance of an ad hoc inference algorithm based on a naïve Bayes classifier: It uses the data publicly disclosed by the individuals who are bound with the target individuals through a social tie (called "friends") declared on the OSN. To do so, they rely on a sample of N ≈ 35k FB profiles from the Dallas/Forth Worth network. Zheleva and Getoor study the same problem but also study gender inference and consider more fine-grained political orientations (i.e., six labels instead of two) BIB005 ]. In addition, they design an algorithm that further exploits the notion of social groups in the inference process. They evaluate the performance of the algorithms on four different datasets including a sample of N ≈ 1.5k FB profiles in the U.S. for political orientation (N ≈ 1k for gender). Jernigan and Mistree focus on sexual orientation inference BIB006 . By analyzing a dataset of N ≈ 6k FB user profiles of university students, they found that the proportion of gay male friends was significantly higher for gay males than for other individuals. By relying A Survey on Interdependent Privacy 122:13 on a logistic regression-based classifier, they demonstrate that this can be used to accurately infer a male individual's sexual orientation. Sarigol et al. also study sexual orientation inference for both users and non-users 16 of OSNs BIB016 . They focused on a dataset of Friendster profiles (N ≈ 1M) in the U.S. Their results show a high accuracy and that the privacy leakage depends on the size of the group individuals belong to (i.e., there is a higher risk for minorities like homosexual males) and on the tendency of others to disclose their sexual orientation. Mislove et al. consider generic attribute inference and propose an approach based on social communities BIB007 . They design an inference algorithm that first identifies communities that are centered on certain attributes (including gender and university) based on the structure of the social graph and the values of the attributes disclosed by some of the users (so-called "seeds"). The algorithm then uses these communities to infer the values of the attributes for the users who do not disclose them. The authors evaluate their approach on several datasets of university students and focus on attributes related to student life, for example college, matriculation year (which is related to age), and major. Thomas et al. also consider generic attribute inference and propose an approach that takes into account the strength of the social ties between users, based on the working assumption that the correlation between the attributes of two individuals increases with the strength of their social ties (which they confirm by analyzing a large dataset) BIB008 . They use the number of common friends as a proxy for the strength of the social tie between two users. They rely on a logistic/linear regression-based classifier. They focus their evaluation on the following attributes: gender, political orientation, religious denomination, relationships status (e.g., single, married) and interests (e.g., movies, music); and they rely on two datasets of FB users in the U.S. (N ≈ 43k and 41k, respectively). Their results show that the information about the attributes of a users' friends substantially improves the accuracy of the inference process. Pesce et al. propose a similar approach but use photo tags as a proxy to the strength of the social ties between users [Pesce et al. 2012]. They focus their evaluation on the following attributes: age, gender, city and country; and they rely on a dataset of N = 664 FB users in Brazil and India. Kotyuk and Buttyan also consider generic attribute (numerical and nominal) inference, and they propose an approach that takes into account the correlation between the private attributes to be inferred BIB010 . They rely on a multi-layer perceptron (MLP); their approach can achieve both regression and classification. They focus their evaluation on the following attributes: age, gender and marital status; and they rely on a dataset of N ≈ 13k users of iwiw, 17 a popular Hungarian OSN. Zamal et al. focus on the inference of three attributes covered by homophily: gender, age, and political affiliation BIB011 . By relying on a support vector machine classifier and on a dataset of Twitter profiles, the authors show that (i) similar inference accuracy can be reached for age and political affiliation by using only friends' data instead of the target user's data, and (ii) by combining the user's and their friends' data, the inference accuracy substantially increases. Dey et al. focus on age inference BIB012 ]. More specifically, they design an algorithm to infer the birth year of an OSN user, based on those of their friends and the friends of their friends (and so on). The authors design an ad hoc iterative inference algorithm that propagates the information about a user's birth year to their friends by using regression techniques. They evaluate the performance of the proposed inference algorithms by using a sample of N ≈ 1.7M FB profiles from the NYC network. Their results show that age can be inferred with an accuracy of less than 2 years for more than 70% of the users. In their follow-up work, Dey et al. focus on the case of high-school students and demonstrate that, by using similar inference techniques and datasets, information hidden by default on the profiles of minors (e.g., birth year) can be inferred from the users' friends' profile information BIB013 . Ryu et al. analyze the inference of hidden attributes based on the local network of users in OSNs  ]. To do so, the authors use three different algorithms and test them on three datasets: Google+ data, UCI FB data, and Duke FB data. The results show that the algorithms have similar performance for the different datasets and, for 75% of users, the algorithms perform much better than random guessing when inferring binary attributes. Gong et al. perform both link prediction and user attribute inference, and they show that attribute inference can further improve link prediction, and conversely, that link prediction can improve attribute inference due to homophily BIB017 ]. The authors demonstrate the superiority of their approach compared to previous work by using Google+ data and by relying on various unsupervised and supervised algorithms, such as support vector machine. Gong and Liu propose to combine friend-based attacks and behavior-based attacks to improve upon previous approaches on attribute inference [Gong and . They focus, in particular, on the inference of three attributes: employer, cities where users have lived, and major. By relying on Google+ and Google Play data with N ≈ 1.1M user profiles, the authors show that their attack's precision outperforms friend-based attacks and behavior-based attacks by 20% and 100%, respectively. Jia et al. further improve upon this attack, both in terms of inference accuracy and algorithmic efficiency, by relying on Markov random fields and loopy belief propagation BIB018 . They evaluate the performance of the attack on a dataset of N ≈ 5.7M Google+/Google Play users. Alsarkal et al. focus on re-identification attacks based on data disclosed by others  ]. More specifically, they investigate to which extent an individual can be re-identified, i.e., can be linked to an identity in the real world, based on this information about them revealed by other individuals (i.e., "co-disclosed" according to the terminology used by the authors). They rely on Shannon's entropy-measured in bits-to capture the re-identifiability of an individual. They use as a baseline the case where individuals disclose information only about themselves (i.e., "selfdisclosure"). Using a dataset of N = 1,357 Twitter user accounts, they demonstrate that an individual's first name, age, and zip code are significantly more often self-disclosed than co-disclosed and that an individual's birthday and gender are significantly more co-disclosed than self-disclosed. They found no significant difference (between self-and co-disclosure) for family relationships. They also demonstrate that the privacy loss (in bits) caused by co-disclosure is substantial; up to two times more than that caused by self-disclosure in some cases. As part of a study of data-collection practices of FB third-party apps, Wang et al. identify an interdependent privacy risk related to OSN data BIB009 ]. The risk comes from the fact that, on some OSNs, including FB, third-party apps can request not only access to a user's data but also to that of their friends (e.g., profile information such as demographics). And only the consent of the user is required; not that of their friends, thus creating an interdependent privacy risk (note that FB's permission system has changed significantly since this work was conducted). Their analysis of a dataset of FB applications shows that 148 out of 1,305 apps (11.3%) ask for friends' personal information. This problem was also noted by four participants (out of 11 from an undergrad students population in the U.S.) during interviews, stating "[the user's] friends never download or agreed to the application's terms" and "[the user] does not own [his or her] friend's information." Biczók and Chia further investigate this problem BIB014 . Their analysis of a dataset of FB apps shows that 518 out of about 27k apps (1.92%) ask for friends' personal information, and that 18k applications (67%) request for basic information of user of the app; this information reveals also her list of friends. This specific interdependent privacy risk was recently put under the spotlight with the Cambridge Analytica scandal  . Humbert et al. study to which extent a social network user can be found through others' friend lists due the fact that hiding their own friend list does not prevent their profile to appear in their friends' friend list BIB015 ]. In particular, the authors propose a new navigation attack to discover users (even those who do not appear in the search directory) by relying on some known attributes about them (such as place of residence, workplace, or alma mater). They demonstrate through experiments on Facebook and Google+, that the majority of users can be found by crawling a median number of user profiles smaller than 400 and 300, respectively.