AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields
By Jinyuan Jia, Binghui Wang, Le Zhang, Neil Zhenqiang Gong. 2017
102 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Neil Zhenqiang Gong
- Binghui Wang
- Yang Zhang
- Jinyuan Jia
- Michael Backes
- Ahmed Salem
- Hao Fu
- Minghong Fang
In Information Systems
- Neil Zhenqiang Gong
- Binghui Wang
- Jinyuan Jia
- Hao Fu
- Minghong Fang
- Guolei Yang
- Jia Liu
- Michael Backes
Citations in the corpus
- Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “LinLBP and LBP based methods were shown to outperform RW-based methods [37, 66].”
- Poisoning Attacks to Graph-Based Recommender Systems (2018, background): “Such attacks are called attribute inference attacks [9] and have been demonstrated to be feasible by multiple studies [9–11, 16, 18, 33].”
- Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “Since LBP-based methods outperform RW-based methods [17], [20], [35], [51], we will focus on LBP-based methods for simplicity.”
- Attribute Inference Attacks in Online Social Networks (2018, methodology): “[30] recently proposed AttriInfer, a Markov Random Field (MRF)-based method to infer attributes in social networks.”
- AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning (2018, methodology): “Attribute inference attacks are emerging threats to user privacy in various application domains ranging from social media [1–7] to recommender systems [8, 9] to mobile platforms [10, 11].”
- MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (2019, background): “Other than membership inference attacks, many other attacks rely on machine learning classifiers, e.g., attribute inference attacks [11, 17, 28], website fingerprinting attacks [7, 22, 29, 46, 67], side-channel attacks [73], location attacks [5, 45, 52, 72], and author identification attacks [8,…”
- GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “[26] recently linearized LBP over a pMRF on an undirected graph.”