arXiv:2605.01616v2 Announce Type: replace Abstract: Human behavior is challenging to measure continuously at scale, yet traces of daily routines and well-being may be reflected in interactions with personal devices. We investigate whether encrypted smartphone network traffic can serve as a passive sensing signal for behavioral states related to sleep disturbance, stress, and loneliness. To capture both population-level patterns and individual-specific behavior, we employ a transformer-based model with user-specific adapters that learns representations of network activity while accounting for p
The proliferation of smartphones and advancements in AI, particularly transformer models, are enabling new methods of inferring human behavior from digital traces.
This development allows for continuous, passive measurement of human behavioral states, offering unprecedented insights into individual and population well-being with significant implications for privacy, health, and targeted interventions.
The ability to infer behavioral signals like stress, sleep disturbance, and loneliness directly from encrypted network traffic fundamentally alters how human behavior can be monitored and understood without explicit user input.
- · Digital health platforms
- · Advertisers
- · Social scientists
- · Security agencies
- · Privacy advocates
- · Individuals with privacy concerns
- · Traditional behavioral research methods
Behavioral states can be continuously, passively monitored from smartphone network activity.
This data could be used for personalized health interventions or highly targeted marketing, raising significant ethical debates.
The widespread deployment of such technologies could lead to a 'surveillance capitalism' model where individual well-being and psychological states are commodity data points.
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Read at arXiv cs.LG