To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

By Elena Zheleva, Lise Getoor. 2009

645 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Privacy of users in social network is a well studied topic in the security community [2, 3, 1, 4].”
Attribute Inference Attacks in Online Social Networks (2018, background): “Existing attribute inference attacks can be roughly classified into two categories, friend-based [15, 23, 27, 36, 40, 46, 57, 64] and behavior-based [10, 35, 42, 60].”
PrivateClean: Data Cleaning and Differential Privacy (2016, background): “Thus, it is important to have privacy mechanisms with formal guarantees about resilience to attack without sacriﬁcing too much utility.”
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, methodology): “Zheleva and Getoor [Zheleva and Getoor 2009] map attribute inference to a relational classification problem.”
Collective aspects of privacy in the Twitter social network (2018, background): “Previous research has extensively evaluated privacy risks for users of social media, in general by evaluating the inference of personal attributes of users from their digital traces [12].”
Children Seen But Not Heard: When Parents Compromise Children's Online Privacy (2015, result): “Similar results were found for age [11], gender [37], and political associations [23], where users’ private traits could be predicted by their friends’ information.”
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning (2018, background): “For simplicity, we assume each entry of x is normalized to be in the range [0,1].”
Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective (2017, background): “While there exists a bulk of research on user privacy traditionally led by the security community [22, 39, 23, 19], studies on private content in visual data began only recently [37, 28, 18].”
A critical look at the evaluation of GNNs under heterophily: are we really making progress? (2023, methodology): “This approach is inspired by LINK (Zheleva & Getoor, 2009) — a linear model using the adjacency matrix rows as features — and is very similar to the recently proposed LINKX model (Lim et al., 2021), which also combines node features and adjacency matrix rows, but uses a custom model.”
A Survey of Signed Network Mining in Social Media (2015, background): “However, most social media users do not share too much of their information [Zheleva and Getoor 2009].”
You are who you know: inferring user profiles in online social networks (2010, background): “Zheleva and Getoor [24] explored inferring user attributes in a social network, using a number of different user profile elements.”
You Are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors (2016, methodology): “Zheleva and Getoor [46] studied various approaches to consider both social links and groups that users joined to perform attribute inference.”
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods (2021, methodology): “Only graph topology: label propagation (standard and two-hop) [80, 57], LINK [79].”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, background): “In attribute inference attacks [31], [32], [34], [35], the graph is the social graph between users; a node is positive if the node has a given attribute (e.”
Combating Attacks and Abuse in Large Online Communities (2016, background): “Various techniques have been prop osed to compromise user anonymity and infer users’ sensitive information from social network data [158, 152, 253, 43].”
AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields (2017, background): “One problem of increasing interest revolves around these private attributes [16, 19, 34, 28, 13, 22, 18, 32, 4, 17, 12].”