We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57 of the users, via confidence estimation, we are able to increase the attack success rate to over 90 if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.
@cite_33 used the information about the musics users like to infer attributes. They augmented the musics with the corresponding Wikipedia pages and then used topic modeling techniques to identify the latent similarities between musics. A user is predicted to share attributes with those that like similar musics with the user. @cite_21 tried to infer various attributes based on the list of pages that users liked on Facebook. Similar to the work performed by @cite_36 , they constructed a feature vector from the Facebook likes and used Logistic Regression to train classifiers to distinguish users with different attributes. @cite_12 constructed a model to infer household structures using users' viewing behaviors in Internet Protocol Television (IPTV) systems, and they showed promising results.
@cite_36 @cite_21 @cite_33 @cite_12
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User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. Focusing on gender, we design techniques for effectively adding ratings to a user's profile for obfuscating the user's gender, while having an insignificant effect on the recommendations provided to that user. We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88 of cases, African Americans and Caucasian Americans in 95 of cases, and between Democrat and Republican in 85 of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy. Suppose that a Facebook user, whose age is hidden or missing, likes Britney Spears. Can you guess his her age? Knowing that most Britney fans are teenagers, it is fairly easy for humans to answer this question. Interests (or "likes") of users is one of the highly-available on-line information. In this paper, we show how these seemingly harmless interests (e.g., music interests) can leak privacy sensitive information about users. In particular, we infer their undisclosed (private) attributes using the public attributes of other users sharing similar interests. In order to compare user-defined interest names, we extract their semantics using an ontologized version of Wikipedia and measure their similarity by applying a statistical learning method. Besides self-declared interests in music, our technique does not rely on any further information about users such as friend relationships or group belongings. Our experiments, based on more than 104K public profiles collected from Facebook and more than 2000 private profiles provided by volunteers, show that our inference technique efficiently predicts attributes that are very often hidden by users. To the best of our knowledge, this is the first time that user interests are used for profiling, and more generally, semantics-driven inference of private data is addressed. What you watch and when you watch say a lot about you, and such information at the aggregated level across a user population obviously provides significant insights for social and commercial applications. In this paper, we propose a model for inferring household structures based on analyzing users' viewing behaviors in Internet Protocol Television (IPTV) systems. We emphasize extracting features of viewing behaviors based on the dynamic of watching time and TV programs and training a classifier for inferring household structures according to the features. In the training phase, instead of merely using the limited labeled samples, we apply semisupervised learning strategy to obtain a graph-based model for classifying household structures from users' features. We test the proposed model on China Telecom IPTV data and demonstrate its utility in census research and system simulation. The demographic characteristics inferred by our approach match well with the population census data of Shanghai, and the inference of household structures of IPTV users gives encouraging results compared with the ground truth obtained by surveys, which opens the door for leveraging IPTV viewing data as a complementary way for time- and resource-consuming census tracking. On the other hand, the proposed model can also synthesize trace data for the simulations of IPTV systems, which provides us with a new strategy for system simulation.