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

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 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.