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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\u2014private attributes can be inferred from users\u2019 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\u2019s publicly available social friends or the user\u2019s behavioral records (e.g., the web pages 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\n            <jats:italic>qualitatively<\/jats:italic>\n            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\n            <jats:italic>the cities a user lived in<\/jats:italic>\n            for 57% of the users; via\n            <jats:italic>confidence estimation<\/jats:italic>\n            , we are able to increase the attack success rate to over 90% if the attacker selectively attacks half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.\n          <\/jats:p>","DOI":"10.1145\/3154793","type":"journal-article","created":{"date-parts":[[2018,1,3]],"date-time":"2018-01-03T13:19:43Z","timestamp":1514985583000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":102,"title":["Attribute Inference Attacks in Online Social Networks"],"prefix":"10.1145","volume":"21","author":[{"given":"Neil Zhenqiang","family":"Gong","sequence":"first","affiliation":[{"name":"Iowa State University, Ames, IA"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"IBM Thomas J. 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