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.
Friend-based attribute inference @cite_13 transformed attribute inference to Bayesian inference on a Bayesian network that is constructed using the social links between users. They evaluated their method using a LiveJournal social network dataset with user attributes. Moreover, it is well known in the machine learning community that Bayesian inference is not scalable. @cite_43 modified Naive Bayes classifier to incorporate social links and other attributes of users to infer some attribute. For instance, to infer a user's major, their method used the user's other attributes such as employer and cities lived, the user's social friends and their attributes. However, their approach is not applicable to users that share no attributes at all.
@cite_43 @cite_13
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In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data. the problems of predicting, classifying, and annotating friends relations in friends networks, based upon network structure and user profile data. First, we document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We document feature analyzers for attributes that depend only on graph attributes, those that depend on individual user demographics and set-valued attributes (e.g., interests, communities, and educational institutions), and those that depend on candidate user pairs. We then extend our data model using whole-network attributes and report machine learning experiments on learning the concept of a connected pair of friends from LiveJournal data. Finally, we develop a theory of dependent types for deriving causal explanations and discuss how this can be used to scale statistical relational learning up to our full corpus, a recent crawl of over a million records from LiveJournal. 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. Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users' public data. In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user's private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user's public data, then the attacker's classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user's public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection.