Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable

2026-05-11

AI Summary

This work addresses the critical privacy–utility trade-off inherent in always-on AI systems that rely on continuous video streams to enhance functionality, yet inadvertently expose sensitive behavioral, emotional, and social information. Existing approaches either target narrow attack models or compromise system utility, while overlooking privacy leakage pathways across the end-to-end data pipeline. For the first time, this study frames privacy preservation in lifelogging video as a foundational challenge for next-generation AI and introduces a pipeline-aware joint optimization framework. By integrating continual perception architectures, world models, and active agent technologies, the framework enables systematic analysis of privacy leakage mechanisms throughout the entire processing flow. The research exposes fundamental limitations of current methods when applied to long-horizon visual data and calls for formal privacy metrics and standardized benchmarks to advance a new paradigm of co-optimized privacy protection and system utility.

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 attack-specific or incur substantial utility loss, and fail to consider the entire data exploitation pipeline. We therefore posit that the privacy-utility trade-off in life-logging video streams is a foundational challenge for next-generation AI systems that demands further investigation. We call for novel pipeline-aware privacy-preserving designs that jointly optimize utility and privacy for long-horizon life-logging visual data. In parallel, formal privacy leakage metrics and standardized benchmarks remain important open directions for future research.

Problem

• life-logging
• privacy-utility trade-off
• always-on AI
• video streams
• privacy leakage

Innovation

• privacy-utility trade-off
• life-logging video
• pipeline-aware privacy
• always-on AI
• formal privacy metrics