Security and Privacy in the Musical Metaverse: Threat Analysis and Design Implications

I. INTRODUCTION

• Delays above 30 ms can impair geographically distributed musical performance, while inconsistent audiovisual or haptic cues can disrupt immersion and artistic expression.These timing and synchronization demands distinguish MM interaction from typical social XR applications.Source: §I · p. 1
• The authors identify a lack of comprehensive security and privacy investigation focused specifically on musical activities in the Metaverse.Existing studies address general Metaverse risks, but may not suffice to design trustworthy musical spaces.Source: §I · p. 1
• MM streams can include audio, video, motion, haptics, environmental sensing, and biometric or neurophysiological signals that reveal identity, behavior, emotion, or health.Real-time creation and exchange of original works also raise concerns about intellectual property, integrity, and unauthorized recording or reproduction.Source: §I · pp. 1–2
• The paper analyzes inherited and MM-specific threats, incorporates stakeholder perspectives, evaluates protocols under latency constraints, and derives security and privacy design implications.Its stated objectives are threat analysis and feasible measures for secure, trustworthy operation.Source: §I · p. 2

II. RELATED WORK

• MM-specific protection must account for timing perturbations, subtle data manipulation, live-content integrity, and architecture-level privacy strategies.The related work describes data minimization, edge or local processing, federated learning, and separating interaction from analytics pipelines as proposed architectural approaches.Source: §II · p. 2
• Prior Metaverse frameworks are not fully sufficient for MM’s ultra-low latency, continuous multimodal interaction, and semantically sensitive data.Related work also identifies challenges for live content protection and real-time creation that static or post-hoc management may not address.Source: §II · pp. 2–3

III. METHODOLOGY

• The analysis combines a state-of-the-art review, contextual system analysis, stakeholder input, and derivation of feasible mitigation measures.The stated aim is to ground the assessment in research and practical constraints of immersive musical interaction.Source: §III · p. 3
• A structured review of research, technical reports, and standards established threats and countermeasures across Metaverse, XR, IoT, and distributed multimedia systems.The review focused on real-time communication, low-latency networking, biometric protection, user safety, and privacy-preserving processing.Source: §III · p. 3
• The contextual analysis examined how ultra-low latency, continuous multimodal streams, expressive performance data, XR collaboration, and heterogeneous devices and networks shape MM risks.It focused on risks tied to tight timing, audio-centric interaction, and sensitive behavioral or physiological data.Source: §III · p. 3
• A structured expert survey used open-ended questions to capture perceived risks, validate literature-based assumptions, and prioritize practical concerns.Mitigations were then derived with attention to performance constraints, architecture, and regulatory considerations.Source: §III · p. 3

Survey participants and procedure

• The survey involved 14 selected experts from 13 organizations, spanning eight industries and five academic institutions.Participants included researchers, engineers, developers, networking and XR experts, audio researchers, and system architects.Source: A. Survey participants and procedure · p. 3
• All participants belonged to the MUSMET consortium, whose partners develop components including edge/cloud infrastructure, XR headsets, BCIs, 3D audio, and music platforms.The survey was intended to draw on both implementation and research perspectives across these components.Source: A. Survey participants and procedure · p. 3
• Respondents anticipated likely security, privacy, and IPR challenges; the survey therefore captured perceived future risk, not retrospective incident data.They identified data collected or transmitted by their components, including audio/video, spatial interaction, identity, session metadata, and physiological signals.Source: A. Survey participants and procedure · p. 3
• The survey applied STRIDE and LINDDUN frameworks and asked open-ended questions about risks, misuse, design trade-offs, planned safeguards, and trust.Two authors independently coded responses through inductive thematic analysis and discussion; risk priority reflected frequency and perceived severity.Source: A. Survey participants and procedure · p. 3

IV. RESULTS

• The results are organized into ecosystem characterization, stakeholder risk prioritization, layered threat analysis, and protocol and architecture assessment.Source: §IV · p. 4

A. Security- and privacy-relevant characteristics of the ecosystem

• The protected assets include live musical content, expressive interaction data, identity and session metadata, intellectual property, and low-latency service availability.Relevant stakeholders include performers, composers, audiences, teachers and students, and platform operators across live and asynchronous activities.Source: A. Security- and privacy-relevant… · p. 4
• MM security objectives include real-time consistency, synchronization integrity, and protection against inference from high-resolution behavioral data.Timing precision, spatial coherence, and interaction fidelity are critical because disruptions can affect user experience and artistic outcomes.Source: A. Security- and privacy-relevant… · p. 4
• Threat actors include external attackers, malicious participants, compromised or semi-trusted services, and observers inferring sensitive information from accessible streams.Privacy risks can arise through correlation, aggregation, and inference across audio, motion, and physiological signals, not only explicit breaches.Source: A. Security- and privacy-relevant… · p. 4
• Neurophysiological data can reveal cognitive or emotional states and require strict safeguards, while expressive data can enable profiling or re-identification.Aggregated session logs and device identifiers can also contribute to long-term tracking and linkage.Source: A. Security- and privacy-relevant… · p. 4
• Ultra-low latency, continuous data exchange, heterogeneous infrastructure, and evolving sessions constrain conventional computation-heavy protections on latency-critical paths.These conditions create performance-protection trade-offs and motivate performance-aware, context-dependent strategies.Source: A. Security- and privacy-relevant… · p. 4

B. Stakeholder risk prioritization

• Identity spoofing, data tampering, and denial-of-service are major security threats because they can undermine trust, degrade performance, or interrupt sessions.Even micro-timing shifts can degrade performance or bias adaptive systems, and short interruptions may irreversibly affect performances or collaborative sessions.Source: B. Stakeholder risk prioritization · p. 4
• Expressive and neurophysiological data can enable re-identification across sessions or platforms, while users may not understand the extent of sensing and processing.Stakeholders also raised regulatory concerns about handling sensitive physiological or behavioral signals.Source: B. Stakeholder risk prioritization · p. 4
• Neurophysiological data leakage and real-time stream disruption rank highest, while IP infringement and avatar impersonation are medium-priority concerns.The prominence of privacy leakage and availability risks over repudiation reflects MM’s emphasis on interaction continuity and data sensitivity.Source: B. Stakeholder risk prioritization · pp. 4–5
• Respondents called for performance-aware security, zerotrust-aligned identity management, coordinated governance, and clearer transparency about sensing and data processing.Their input emphasizes a holistic approach spanning technical, organizational, and user-centric concerns.Source: B. Stakeholder risk prioritization · p. 5

C. Layered threat analysis

• MM threats span interconnected network, application, data-processing, device, IP, and social components, and can propagate across layers.Tight coupling among real-time interaction, multimodal data, and distributed architectures can amplify their impact.Source: C. Layered threat analysis · p. 5

1) Network and transport layer:

• Network threats include interception, packet injection or reordering, denial-of-service, session hijacking, and metadata exposure that can reveal collaboration patterns.Deliberate delay or packet reordering can degrade synchronization without fully interrupting communication.Source: 1) Network and transport layer · p. 5

2) Application layer:

• Application-layer threats include avatar impersonation, unauthorized access, environment manipulation, recording, redistribution, and misleading information that can subtly disrupt coordinated musical activity.Small changes to spatial audio, visual cues, or interaction parameters may resemble normal system variability and be difficult to detect.Source: 2) Application layer · p. 5

3) Data and AI/ML layer:

• Data and AI/ML threats include stream leakage, inference and model attacks, poisoning, adversarial inputs, and privacy risks from centralized or long-term data use.Expressive data can enable profiling, imitation, re-identification, or inference of emotional, cognitive, and physical states; distributed learning reduces raw-data exposure but can leak through shared updates.Source: 3) Data and AI/ML layer · pp. 5–6

4) Device and sensor layer:

• Device and sensor threats include compromise, spoofed inputs, exposed device communications, side channels, and inconsistent security across heterogeneous or unpatched hardware.Attacks on BCI or affective sensing can infer or alter sensitive cognitive or emotional information, with potential privacy violations or unintended behavioral influence.Source: 4) Device and sensor layer · p. 6

5) IPR and content layer:

• IPR and content threats include unauthorized recording or redistribution, altered creative works, fraudulent attribution, appropriation of collaborators’ contributions, retained ephemeral content, and lost rights metadata.These risks can cause economic loss, weaken ownership rights, or expose performances and rehearsals to long-term misuse.Source: 5) IPR and content layer · p. 6

6) Social and user safety layer:

• Social and user-safety threats include harassment, social engineering, unwanted interaction, disturbing content, exclusion, and psychological or cognitive risks.Immersive presence may amplify aggression, while prolonged exposure or stress-inducing stimuli may affect wellbeing, perception, or decision-making.Source: 6) Social and user safety layer · p. 7
• Effective protection requires a holistic cross-layer approach that accounts for interactions among timing constraints, data sensitivity, and interaction semantics.Table III summarizes how attack vectors map to system components and their implications in MM environments.Source: 6) Social and user safety layer · p. 7

D. Protocol and architecture assessment

• Ultra-low-latency musician-to-musician paths require security that preserves deterministic behavior and minimal jitter, unlike less time-critical audience-facing paths.Latency-critical paths typically require below 30 ms end-to-end delay, limiting protocols that rely on retransmission or buffering.Source: D. Protocol and architecture assessment · p. 7
• TLS over TCP is generally unsuitable for ultra-low-latency interaction because of head-of-line blocking and retransmission delays; SRTP and DTLS over UDP better suit real-time streams.DTLS avoids TCP blocking while maintaining comparable security guarantees, though handshake and key-management overhead need control; SRTP offers predictable per-packet overhead. QUIC and HTTP/3 suit audience delivery better than real-time interaction, while scrambling cannot replace end-to-end protection.Source: D. Protocol and architecture assessment · pp. 7–8
• MM systems need hybrid security strategies and co-design of protocols, architecture, and application requirements across latency zones and data sensitivities.Separate latency-critical paths from control, signaling, and analytics; process sensitive expressive and biometric data locally or at the edge, and use minimization, purpose limitation, and distributed or federated processing.Source: D. Protocol and architecture assessment · p. 8

V. DISCUSSION

• Subtle jitter, microdelays, or distortions can impair musical coordination below conventional fault-detection thresholds, so threat models must include low-amplitude, high-impact attacks.Timing sensitivity reshapes attack severity in musical interaction.Source: §V · p. 8
• Expressive interaction data can enable re-identification and behavioral inference, while biometric and neurophysiological data add risks of irreversible privacy exposure.These data are integral to system functionality, requiring context-dependent, multi-layered protection rather than uniform policies.Source: §V · p. 8
• Real-time creation and exchange make intellectual-property protection a technical concern because unauthorized recording, manipulation, or lost provenance can harm artistic integrity and economic value.Protection needs to operate during interaction, not only after it.Source: §V · p. 8
• Threats span network, application, data/AI, device, intellectual-property, and social layers, while ultra-low latency, continuous streams, and heterogeneous infrastructure constrain conventional security approaches.The discussion calls for technically robust solutions compatible with performance constraints.Source: §V · p. 8
• Musician-to-musician paths need lightweight, stream-oriented protection such as SRTP and DTLS, whereas less time-critical audience-facing paths can use more robust protocols such as QUIC/HTTP3 and TLS.The paper concludes that no single mechanism fits every interaction context.Source: §V · pp. 8–9, Figure 2 · p. 9
• Harassment, impersonation, and manipulative interactions can undermine trust and continued participation, motivating safeguards that also promote transparency and user agency.Source: §V · p. 9
• The stakeholder risk prioritization reflects 14 experts from one research consortium and may underrepresent independent researchers, outside practitioners, and end users.It should be read as an expert-informed assessment, not a statistically representative characterization of the broader MM community.Source: §V · p. 9

A. Design implications

• Use lightweight, deterministic, stream-oriented protection on latency-critical paths and reserve heavier controls for storage, analytics, and non-real-time components.Ultra-low-latency paths should avoid buffering, retransmissions, or complex handshakes.Source: A. Design implications · pp. 9–10
• Separate latency-critical interaction from control, signaling, and analytics so systems can apply differentiated security and stronger protection where timing constraints are less stringent.Source: A. Design implications · p. 9
• Minimize expressive and biometric data, limit its use to necessary purposes, and process sensitive streams locally or at the edge to reduce exposure and aggregation risks.The guidance calls for protection calibrated to each data type's sensitivity.Source: A. Design implications · pp. 9–10
• Protect artistic content during creation and interaction against unauthorized recording, manipulation, and redistribution while preserving integrity and attribution in real time.Source: A. Design implications · p. 9
• Support dynamic participation with strong authentication, context-aware authorization, and safeguards for avatar-based identities across heterogeneous environments.Source: A. Design implications · p. 9
• Integrate security and privacy from the outset with explicit trust boundaries, minimal implicit trust, clear consent, and user visibility and control over data sharing.Transparency is especially emphasized for biometric sensing.Source: A. Design implications · p. 10

• Neurophysiological data leakage and real-time stream disruption are the most critical identified risks among assets including live musical content, expressive interaction data, identity and session metadata, IP, and service availability.Source: §VI · p. 10
• Vulnerabilities span network, application, data/AI, device, IPR, and social dimensions, calling for a holistic, cross-layer security approach.Source: §VI · p. 10
• Under strict latency constraints, TLS over TCP is generally unsuitable for real-time musical interaction, while SRTP and DTLS provide a better balance between protection and performance.No single protocol or architectural solution fits all MM scenarios; security should adapt to latency constraints, data sensitivity, and interaction contexts.Source: §VI · p. 10
• Effective MM systems should integrate latency-aware protection, data minimization, edge-centric processing, explicit trust boundaries, and user awareness mechanisms.Source: §VI · p. 10
• Open challenges include governance of biometric and neurophysiological data, interoperable IP-rights enforcement across platforms, and scalable trust management in distributed, multi-stakeholder ecosystems.Source: §VI · p. 10
• Security and privacy are fundamental requirements for meaningful, high-quality, and trustworthy musical interaction.Source: §VI · p. 10