论文简报
cs.IT 2605.28325v1 值得读

ISAC Privacy: Challenges and Solutions for 6G

Onur Günlü, Stefano Tomasin, João P. Vilela, Francesco Chiti, Prajnamaya Dass, Angeliki Alexiou, Utz Roedig

发布日期：2026-05-27 11:25 相关性：1.0000 价值：0.7000 分类：cs.IT cs.CR eess.SP eess.SY
arXiv 摘要 PDF 原文 网页正文

摘要

Integrated sensing and communication (ISAC) is a promising feature of future communication networks. While spatial sensing can improve network performance and enable external services, it also creates privacy challenges that go beyond the confidentiality of communication content. Future networks using millimeter-wave (mmWave) and sub-terahertz (THz) frequencies may collect or infer detailed information about people, devices, bystanders, passive objects, and environments in a sixth-generation (6G) deployment area. Such sensing can reveal location and environment data, support behavioral profiling such as movement or activity recognition, and, in advanced cases, expose physiological information such as breathing frequency or heart-rate-related data. Thus, the capabilities of spatial sensing must be controlled to satisfy privacy requirements. In this work, we organize privacy-sensitive ISAC data into three sensing levels: location and environment data, behavioral data, and physiological data, and use this classification as the organizing principle throughout the paper. Based on this classification, we discuss internal and external ISAC applications, identify privacy challenges related to consent, transparency, data ownership, profiling, bystander exposure, and sensitive sensing data, review representative solution directions, and outline future research directions for privacy-preserving ISAC.

相关性判断

high
相关方向
wireless_communications integrated_sensing_communication privacy 6g_networks
判断依据

The paper is about integrated sensing and communication (ISAC) for 6G, with privacy challenges and solutions tied to sensing, localization, and communication systems, which is directly adjacent to information theory and communications.

价值判断

High relevance to cs.IT wireless/6G intelligence because it targets ISAC privacy risks in mmWave/THz sensing networks. The three-level sensing-data taxonomy gives a useful organizing frame for location/environment, behavioral, and physiological leakage. Technical value is broad but survey-like, covering physical-layer controls, RIS, privacy-preserving ML, and governance rather than a single new method.

核心问题与主要方法

核心问题

How to characterize and mitigate privacy risks created by 6G ISAC sensing of users, bystanders, objects, and environments.

场景：6G integrated sensing and communication over mmWave/THz links with large antenna arrays, directional beams, and sensing by base stations, cooperative nodes, or UEs.

主要方法

Three-level sensing-data taxonomy: L1 location/environment, L2 behavioral, L3 physiological/biometric information. Application-to-risk mapping that separates internal ISAC uses from external sensing services and tracks which privacy level each use case implicates. Governance framing around consent, transparency, purpose limitation, data ownership, profiling, bystander exposure, and accountability. Layered mitigation survey: physical-layer observability controls for L1, representation/federated/privacy-preserving learning for L2, and stricter lifecycle/access-control architectures for L3.

关键贡献与后续阅读

关键贡献

Defines ISAC privacy around sensing-derived personal and environmental information, clearly separating it from communication-content confidentiality. Introduces and consistently applies a three-level taxonomy of privacy-sensitive ISAC data: location/environment, behavioral, and physiological information. Connects 6G sensing enablers such as mmWave/sub-THz frequencies, large antenna arrays, directional beams, cooperative nodes, RIS, and UE-assisted sensing to specific privacy risks. Organizes representative mitigation directions by leakage level, including spatial observability control for L1, privacy-preserving representations and federated learning for L2, and policy-based architectural controls for L3. Articulates a research roadmap combining physical-layer design, data minimization, behavioral/physiological inference control, and governance mechanisms for privacy-preserving ISAC.

研究启发

Does the full paper provide a precise comparison table or taxonomy that distinguishes prior ISAC privacy surveys from this three-level framing? Are any of the cited physical-layer privacy mechanisms evaluated under common ISAC assumptions, or are they only summarized qualitatively? Does the roadmap define actionable privacy metrics for L1/L2/L3 leakage, or only identify this as an open problem?

限制与不确定性

Structure evidence indicates no formal guarantees, bounds, or quantitative evaluation in the provided analysis. Likely more valuable as a roadmap or taxonomy paper than as a deep technical contribution.

参考文献

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