Divide-and-Learn: A Random Indexing Approach to Attribute Inference Attacks in Online Social Networks - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Divide-and-Learn: A Random Indexing Approach to Attribute Inference Attacks in Online Social Networks

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Abstract

We present a Divide-and-Learn machine learning methodology to investigate a new class of attribute inference attacks against Online Social Networks (OSN) users. Our methodology analyzes commenters' preferences related to some user publications (e.g., posts or pictures) to infer sensitive attributes of that user. For classification performance, we tune Random Indexing (RI) to compute several embeddings for textual units (e.g., word, emoji), each one depending on a specific attribute value. RI guarantees the comparability of the generated vectors for the different values. To validate the approach, we consider three Facebook attributes: gender, age category and relationship status, which are highly relevant for targeted advertising or privacy threatening applications. By using an XGBoost classifier, we show that we can infer Facebook users' attributes from commenters' reactions to their publications with AUC from 94% to 98%, depending on the traits.
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Dates and versions

hal-03463902 , version 1 (02-12-2021)

Licence

Attribution - CC BY 4.0

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Sanaz Eidizadehakhcheloo, Bizhan Alipour Pijani, Abdessamad Imine, Michaël Rusinowitch. Divide-and-Learn: A Random Indexing Approach to Attribute Inference Attacks in Online Social Networks. 35th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2021, Calgary, AB, Canada. pp.338-356, ⟨10.1007/978-3-030-81242-3_20⟩. ⟨hal-03463902⟩
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