A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
[article]
2020
arXiv
pre-print
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses
arXiv:1805.04810v2
fatcat:ijtcty5pgja6rdep5awx25m2um