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

• URL: http://arxiv.org/abs/2605.10404v1
• Date: Mon, 11 May 2026 11:42:43 GMT
• Title: Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
• Abstract summary: Life-logging visual sensing is becoming inevitable, forming the backbone of persistent, always-on AI systems.<n>Privacy risks may undermine public trust and hinder the sustainable development of always-on AI technologies.<n>We call for novel pipeline-aware privacy-preserving designs that jointly optimize utility and privacy for long-horizon life-logging visual data.
• Score: 22.843152964254273
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Position: Embodied AI Requires a Privacy-Utility Trade-off [18.27271917317112]
  We propose Secure Privacy Integration in Next-generation Embodied AI (SPINE)<n>SPINE treats privacy as a dynamic control signal governing cross-stage coupling throughout the entire EAI life cycle.<n>We conduct preliminary simulation and real-world case studies to conceptually validate how privacy constraints propagate downstream to reshape system behavior.
  arXiv  Detail & Related papers  (2026-05-06T15:16:05Z)
• Neural Gate: Mitigating Privacy Risks in LVLMs via Neuron-Level Gradient Gating [71.55435880263238]
  We introduce Neural Gate, a novel method for mitigating privacy risks through neuron-level model editing.<n>Our method improves a model's privacy safeguards by increasing its rate of refusal for privacy-related questions.
  arXiv  Detail & Related papers  (2026-03-13T03:03:20Z)
• Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence [55.512671026669516]
  We propose the Single-Pixel Vision-Language Model (SP-VLM), a novel framework that reimagines secure environmental monitoring.<n>It achieves intrinsic privacy-by-design by capturing human dynamics through inherently low-dimensional single-pixel modalities.<n>We show that SP-VLM can nonetheless extract meaningful behavioral semantics, enabling robust anomaly detection, people counting, and activity understanding.
  arXiv  Detail & Related papers  (2026-01-21T09:11:26Z)
• Adversary-Aware Private Inference over Wireless Channels [51.93574339176914]
  AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.<n>As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations.<n>We propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
  arXiv  Detail & Related papers  (2025-10-23T13:02:14Z)
• Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs [0.0]
  We propose a Privacy-Enhancing Technology (PET) framework for wearable devices.<n>We integrate federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology.<n>Our framework reduces privacy risks by up to 70 percent while preserving data utility and performance.
  arXiv  Detail & Related papers  (2025-03-05T12:01:22Z)
• Privacy-Preserving Video Anomaly Detection: A Survey [14.867137878046536]
  Video Anomaly Detection (VAD) aims to automatically analyze patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm.<n>Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives.<n>This article is the first to systematically reviews the progress of P2VAD, defining its scope and providing an intuitive taxonomy.
  arXiv  Detail & Related papers  (2024-11-21T20:29:59Z)
• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective [28.968233485060654]
  We discuss the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.
  arXiv  Detail & Related papers  (2023-11-30T05:03:08Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection [59.04634695294402]
  Video anomaly detection (VAD) without human monitoring is a complex computer vision task. Privacy leakage in VAD allows models to pick up and amplify unnecessary biases related to people's personal information. We propose TeD-SPAD, a privacy-aware video anomaly detection framework that destroys visual private information in a self-supervised manner.
  arXiv  Detail & Related papers  (2023-08-21T22:42:55Z)
• Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety [2.2427353485837545]
  This paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. We propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications.
  arXiv  Detail & Related papers  (2023-02-08T19:54:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.