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You Are Who You Know: Inferring User Profiles in Online Social Networks
"... 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 ..."
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Cited by 118 (8 self)
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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.
Privacy wizards for social networking sites
- in WWW ’10: Proceedings of the 19th International World Wide Web Conference
, 2010
"... 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 ..."
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Cited by 95 (2 self)
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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.
Inferring private information using social network data
, 2008
"... Online 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 a ..."
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Cited by 48 (2 self)
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Online 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 propose an effective, scalable inference attack for released social networking data to infer undisclosed private information about individuals. We then explore the effectiveness of possible sanitization techniques that can be used to combat such an inference attack. 1
Abusing Social Networks for Automated User Profiling
"... Abstract. Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored on these sites calls for appropriate security precautions to protect this data. In this paper, we describe how we are able to take advantage of a common weaknes ..."
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Cited by 46 (3 self)
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Abstract. Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored on these sites calls for appropriate security precautions to protect this data. In this paper, we describe how we are able to take advantage of a common weakness, namely the fact that an attacker can query popular social networks for registered e-mail addresses on a large scale. Starting with a list of about 10.4 million email addresses, we were able to automatically identify more than 1.2 million user profiles associated with these addresses. By automatically crawling and correlating these profiles, we collect detailed personal information about each user, which we use for automated profiling (i.e., to enrich the information available from each user). Having access to such information would allow an attacker to launch sophisticated, targeted attacks, or to improve the efficiency of spam campaigns. We have contacted the most popular providers, who acknowledged the threat and are currently implementing our proposed countermeasures. Facebook and XING, in particular, have recently fixed the problem. 1
Co-evolution of social and affiliation networks
- In 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD
, 2009
"... In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of real-world networks related only to node-to-node link formation. We ..."
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Cited by 38 (2 self)
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In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of real-world networks related only to node-to-node link formation. We propose a novel model which captures the coevolution of social and affiliation networks. We provide surprising insights into group formation based on observations in several real-world networks, showing that users often join groups for reasons other than their friends. Our experiments show that the model is able to capture both the newly observed and previously studied network properties. This work is the first to propose a generative model which captures the statistical properties of these complex networks. The proposed model facilitates controlled experiments which study the effect of actors ’ behavior on the network evolution, and it allows the generation of realistic synthetic datasets.
Measuring Privacy Risk in Online Social Networks
"... 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 provi ..."
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Cited by 35 (0 self)
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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. 1.
Cold Start Link Prediction
"... Inthetraditionallinkpredictionproblem, asnapshotofasocial network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely to appear in the future. In this paper, we introduce cold start link prediction as the problem of predicting the structure of a so ..."
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Cited by 33 (0 self)
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Inthetraditionallinkpredictionproblem, asnapshotofasocial network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely to appear in the future. In this paper, we introduce cold start link prediction as the problem of predicting the structure of a social network when the network itself is totally missing while some other information regarding the nodes is available. Weproposeatwo-phasemethodbasedonthebootstrap probabilistic graph. The first phase generates an implicit social network under the form of a probabilistic graph. The second phase applies probabilistic graph-based measures to produce the final prediction. We assess our method empirically over a large data collection obtained from Flickr, using interest groups as the initial information. The experiments confirm the effectiveness of our approach.
Facebook users have become much more private: a large-scale study
"... Abstract—We investigate whether Facebook users have become more private in recent years. Specifically, we examine if there have been any important trends in the information Facebook users reveal about themselves on their public profile pages since early 2010. To this end, we have crawled the public ..."
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Cited by 22 (1 self)
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Abstract—We investigate whether Facebook users have become more private in recent years. Specifically, we examine if there have been any important trends in the information Facebook users reveal about themselves on their public profile pages since early 2010. To this end, we have crawled the public profile pages of 1.4 million New York City (NYC) Facebook users in March 2010 and again in June 2011. We have found that NYC users in our sample have become dramatically more private during this period. For example, in March 2010 only 17.2 % of users in our sample hid their friend lists, whereas in June 2011, just 15 months later, 52.6 % of the users hid their friend lists. We explore privacy trends for several personal attributes including friend list, networks, relationship, high school name and graduation year, gender, and hometown. We find that privacy trends have become more pronounced for certain demographics. Finally, we attempt to determine the primary causes behind the dramatic decrease in the amount of information Facebook users reveal about themselves to the general public. I.
D.M.: Unfriendly: multi-party privacy risks in social networks
- In: Proceedings of the 10th International Conference on Privacy
, 2010
"... Abstract. 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 o ..."
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Cited by 22 (0 self)
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Abstract. 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. 1
Multiparty authorization framework for data sharing in online social networks.
- In Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy, DBSec’11,
, 2011
"... Abstract. Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and priv ..."
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Cited by 21 (6 self)
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Abstract. Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide effective mechanisms to enforce privacy concerns over data associated with multiple users. In this paper, we propose a multiparty authorization framework that enables collaborative management of shared data in OSNs. An access control model is formulated to capture the essence of multiparty authorization requirements. We also demonstrate the applicability of our approach by implementing a proof-of-concept prototype hosted in Facebook.
