You are what you like! Information leakage through users' Interests
By Chaabane Abdelberi, Gergely Ács, Mohamed Ali Kâafar. 2012
172 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Binghui Wang
- Neil Zhenqiang Gong
- Jinyuan Jia
- Prateek Mittal
- Ahmed Salem
- Yang Zhang
- Le Zhang
- Michael Backes
Citations in the corpus
- Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Private attributes in a user’s record can be inferred by exploiting the public attributes of other users sharing similar interests [6].”
- Targeted Online Password Guessing: An Underestimated Threat (2016, methodology): “This term is sometimes used inter-changeably with the term“per-sonally identifiable info” [9,17], while sometimes their defi-nitions vary greatly in different situations, laws, regulations [20,23].”
- AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields (2017, background): “Chaabane et al. [4] used the information about the musics users like to infer attributes.”
- AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning (2018, background): “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].”
- Attribute Inference Attacks in Online Social Networks (2018, methodology): “One privacy attack of increasing interest revolves around these user attributes [10, 15, 23, 27, 35, 36, 40, 42, 46, 57, 60, 64].”
- MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (2019, background): “, 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, 41].”
- Web Data Extraction, Applications and Techniques: A Survey (2012, background): “analysis based on geotagged data. The most relevant result is that it is possible to locate Flickr photos with a high precision by identifying landmarks via visual, temporal and textual features. In [Chaabane et al. 2012] the authors considers a range of user signals expressing user interests (e.g.,”