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Quantifying Privacy Risks of Behavioral Semantics in Mobile Communication Services
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AI
DOI：10.1109/TIFS.2025.3533144.png)
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Abstract
En 中文
Location-based mobile services, while improving user daily life, also raise significant privacy concerns in the sharing of location data. These trajectories indicate users' traveling behavioural traces with rich semantics derived from open-source information. Behavioral-semantic analysis reveals users' travelling motivations and underlying behavioral patterns. It contributes to attackers launching inferential attacks for behavior prediction, identity identification, or other privacy invasions, even when the location data is protected. It remains open to the issues of behavioral-semantic privacy-risk quantification and privacy-protection evaluation. This paper aims to reveal such semantic privacy risks of user behaviors arising from the publication of location trajectories in mobile scenarios. We formalize user semantic-mobility process to analyze his underlying behavior patterns. Then, we design semantic inference algorithms conditional on the released trajectory to reason about the observation-based likelihood of the user's actual staying and transfer behaviours and behavioural-trace tracking. Extensive experiments with real-world data demonstrate their performance on inference accuracy and semantic similarity, offering a quantification criterion for deploying mobile privacy protection.
Keywords:
Semantics
Privacy
Trajectory
Protection
Motion pictures
Data privacy
Inference algorithms
Hidden Markov models
Stochastic processes
Accuracy
Mobile service
location privacy protection
behavior semantics
privacy-risk evaluation & quantification
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