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

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.

Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2

“…(The authors of [38] also consider Facebook, and infer sensitive data which is outside our scope). Other examples are [14,21,64,69]. All such works show that AIA can be enacted in the real world, representing a subtle privacy risk.…”