Seeing Isn't Believing: QoE Evaluation for Privacy-Aware Users

More and more network media users concern about their privacy issues since they know that their network behaviors are being observed, and thus the observable users' data are not reliable and sufficient in this case. How to evaluate the true quality of experience (QoE) of the privacy-aware users...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2019-07, Vol.37 (7), p.1656-1665
Main Authors: Zhou, Liang, Wu, Dan, Wei, Xin, Dong, Zhenjiang
Format: Article
Language:English
Subjects:
Correlation
Correlation analysis
Data models
Data privacy
data sparsity
Iterative methods
Media
network media
Privacy
privacy-aware users
QoE
Quality of experience
Reliability
Robustness (mathematics)
Semantics
unreliable data
user classification
User satisfaction
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Summary:More and more network media users concern about their privacy issues since they know that their network behaviors are being observed, and thus the observable users' data are not reliable and sufficient in this case. How to evaluate the true quality of experience (QoE) of the privacy-aware users has become a significant technical challenge because of the most majority of existing data-driven QoE evaluation schemes based on the premise of the true and adequate users' observations. To get over this dilemma, this paper proposes a systematic and robust QoE evaluation scheme with unreliable and insufficient observation data. Specifically, we first translate the subjective privacy-aware QoE evaluation problem into an objective rational user analysis procedure. Then, a semantics-based similarity measurement for multidimensional correlation analysis is constructed to classify the observable data. Subsequently, the highlight of this paper lies in proposing a class-level joint user classification and data cleaning strategy by frequently updating the training processes. Through elaborately designing an iterative framework, it can effectively resolve the data sparsity and inconsistency problems due to the user privacy-aware preferences. Importantly, we also introduce an efficient QoE model construction method for online implementation, and numerical results validate its efficiency for different kinds of privacy-aware users.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2019.2916452