Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
Tianyuan Zou, Liang Yue, Yang Liu, Ya-Qin Zhang, Sijie Cheng

TL;DR
This paper discusses the inevitable privacy-utility trade-off in life-logging video streams from always-on devices, emphasizing the need for pipeline-aware privacy-preserving solutions and standardized benchmarks.
Contribution
It highlights the foundational challenge of balancing privacy and utility in continuous life-logging visual data and calls for novel, pipeline-aware privacy-preserving methods.
Findings
Existing protections are attack-specific or utility-reducing.
Life-logging video streams pose significant privacy risks.
Need for formal metrics and benchmarks for privacy leakage.
Abstract
With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becoming inevitable, forming the backbone of persistent, always-on AI systems. Meanwhile, recent advances in proactive agents and world models signal a fundamental shift from episodic, prompt-driven tools to next-generation AI systems that continuously perceive and react to the physical world. Although life-logging video streams can substantially improve utility of these promising systems, they also introduce significant privacy risks by revealing sensitive information, such as behavioral patterns, emotional states, and social interactions, beyond what isolated images expose. If unresolved, these risks may undermine public trust and hinder the sustainable development of always-on AI technologies. Existing privacy protections are either…
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