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2019

Privacy Preserving QoE Modeling using Collaborative Learning

Ickin, Selim, Vandikas, Konstantinos, Fiedler, Markus

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Machine Learning based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles.

This prevents models from becoming generic.
Consequently, these trained models may under-perform when tested outside the experimented population.
One reason for the limited datasets, which we refer in this paper as small QoE data lakes, is due to the fact that often these datasets potentially contain user sensitive information and are only collected throughout expensive user studies with special user consent.

Privacy Preserving QoE Modeling using Collaborative Learning · Around

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Differential Privacy
J. Braams and C. Dwork · 2011
Earlier work this paper cites.
Quantification of Youtube QoE via crowdsourcing
T. Hoßfeld et al · 2011
Earlier work this paper cites.
Survey on machine learning-based QoE-QoS correlation models
S. Aroussi et al · 2014
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz et al · 2017
Earlier work this paper cites.

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