Publikationen

Autor	Simo, Hervais; Shulman, Haya; Schufrin, Marija; Reynolds, Steven Lamarr; Kohlhammer, Jörn
Datum	2021
Art	Conference Paper
Abstrakt	The European GDPR calls, besides other things, for innovative tools to empower online social networks (OSN) users with transparency over risks of attribute inferences. In this work, we propose a novel transparency-enhancing framework for OSN, PrivInferVis, to help people assess and visualize their individual risks of attribute inference derived from public details from their social graphs in different OSN domains. We propose a weighted Bayesian model as the underlying method for attribute inference. A preliminary evaluation shows that our proposal outperforms baseline algorithms on several evaluation metrics significantly. PrivInferVis provides visual interfaces that allow users to explore details about their (inferred and self-disclosed) data and to understand how inference estimates and related scores are derived.
Konferenz	Conference on Computer Communications (INFOCOM) <2021, Online>
Projekt	Bundesministerium für Bildung und Forschung BMBF (Deutschland)/
Referenz	Institute of Electrical and Electronics Engineers -IEEE-: IEEE INFOCOM 2021, Conference on Computer Communications Workshops (INFOCOM WKSHPS): May 9-12, 2021, virtually. Piscataway, NJ: IEEE, 2021, Art. 9484595, 2 pp.
Schlüssel	ISBN : 9781665447140

PrivInferVis: Towards Enhancing Transparency over Attribute Inference in Online Social Networks