音乐元宇宙中的安全与隐私：威胁分析与设计启示
Security and Privacy in the Musical Metaverse: Threat Analysis and Design Implications
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中文总结 AI 辅助
本文针对音乐元宇宙开展多层威胁分析，结合利益相关者调查评估现有安全协议适用性，提出兼顾安全与实时性能的设计指南。
中文摘要 AI 辅助
音乐元宇宙（MM）为协作音乐交互引入了沉浸式、实时环境，其特点是超低延迟约束、连续多模态数据流以及异构设备。这些特性造就了与传统扩展现实（XR）或多媒体系统显著不同的独特安全与隐私格局。本文对MM生态系统开展多层威胁分析，确定了关键资产，包括实时音乐内容、表达性交互数据、身份与会话元数据以及知识产权。我们从网络、应用、数据/人工智能、设备、知识产权以及社会层面对威胁进行分析，尤其关注表达性和神经生理数据带来的风险，这类数据可用于推断、重新识别以及潜在的隐私侵犯。我们描述了一项涉及13个组织的14名参与者的利益相关者驱动调查，结果显示，神经生理数据泄露和实时流中断被认为是最关键的风险，其次是知识产权侵权和化身冒充。我们进一步评估了现有安全协议在严格延迟约束下的适用性，表明传统方法如基于TCP的传输层安全协议（TLS）通常与实时音乐交互不兼容，而轻量级、面向流的机制（如安全实时传输协议（SRTP）、数据报传输层安全协议（DTLS））在安全与性能之间提供了更合适的平衡。基于这些发现，我们推导了一套MM系统的设计指南，强调感知延迟的安全性、交互路径的差异化、数据最小化以及以边缘为中心的处理。研究结果支持采用设计安全的方法，可在不损害实时性能的前提下实现信任与合规。
英文摘要
The Musical Metaverse (MM) introduces immersive, real-time environments for collaborative musical interaction, characterized by ultra-low-latency constraints, continuous multimodal data streams, and heterogeneous devices. These properties create a distinctive security and privacy landscape that differs significantly from conventional XR or multimedia systems. This paper presents a multi-layer threat analysis of MM ecosystems, identifying key assets including live musical content, expressive interaction data, identity and session metadata, and intellectual property. Threats are analyzed across network, application, data/AI, device, intellectual property rights, and social layers, with particular attention to risks arising from expressive and neurophysiological data, which enable inference, re-identification, and potential privacy violations. We describe a stakeholder-driven survey involving 14 participants from 13 organizations, revealing that neurophysiological data leakage and real-time stream disruption are perceived as the most critical risks, followed by intellectual property infringement and avatar impersonation. We further evaluate the suitability of existing security protocols under strict latency constraints, showing that conventional approaches such as TLS over TCP are often incompatible with real-time musical interaction, while lightweight, stream-oriented mechanisms (e.g., SRTP, DTLS) provide a more suitable balance between security and performance. Based on these findings, we derive a set of design guidelines for MM systems, emphasizing latency-aware security, differentiation of interaction paths, data minimization, and edge-centric processing. The results support a security-by-design approach that enables trust and compliance without compromising real-time performance.
发表机构
- University of Trento（特伦托大学）
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