Abstract
The architecture provisions 6G resources from declared intent categories without traffic inspection and verifies fulfillment using only standardized cell-level performance counters, providing formal privacy bounds and practical verification across operator networks.
Intent-based network management automates 6G service lifecycles, but existing fulfilment and assurance approaches require traffic inspection, per-flow tracking, or vendor-internal telemetry. These requirements conflict with privacy constraints in multi-tenant networks and vendor opacity in O-RAN deployments. We present an architecture that provisions resources from declared intent categories without traffic inspection and verifies fulfilment using only cell-level standardised PM counters at the O1 interface. A data-processing inequality bounds resource-allocation leakage about traffic content by log_2 K bits for K categories. Intent-traffic unlinkability and node-opaque verification hold by construction. If verification requires at least q cells per category among M cells, the ceiling becomes log_2 lfloor M/q rfloor bits. This bound covers the category/resource channel, not total inference from PM counters. When individual records add state-independent noise to the category aggregate, the category-mean verifier is at least as powerful as any per-agent verifier. We map the architecture to the 3GPP intent lifecycle and O-RAN Non-RT RIC. On production PM data from four operator networks, the category-mean score has higher AUC on three networks and comparable AUC on the fourth under category-wide replacement, while per-cell screening suits sparse faults. These injection-based experiments do not establish the theorem's observation premise. Within-period cell co-movement limits a category mean to the evidence of 6 to 14 independent cells, and category-level verifiers need per-category calibration.
Community
Intent-based 6G management usually assumes traffic inspection, per-flow tracking or vendor-internal telemetry. This architecture needs none of them. It provisions from a declared intent category and verifies fulfilment from standard cell-level O1 counters only.
Because the allocation depends on the category alone, a data-processing inequality caps what it reveals about traffic content at log2 K bits for K categories. If verification needs q cells per category among M cells, the ceiling becomes log2 floor(M/q) bits, so the detection requirement sets the privacy ceiling with no separate privacy parameter.
On production counters from four operator networks, detection plateaus beyond moderate K while the leakage ceiling keeps rising. Under category-wide replacement the category-mean verifier has higher AUC on three networks and comparable AUC on the fourth. Per-cell screening suits sparse faults.
Summary, scope and BibTeX: https://jossarmstrong.org/works/privacy-intent/
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- $Z^2$-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN (2026)
- Quantifying the Privacy Posture of Operator-Side 5G/O-RAN Profiles (2026)
- rApp/xApp Attestation: A New Security Use Case for O-RAN (2026)
- Runtime Assurance Under Measurement Attack: Necessary and Sufficient Observability Conditions for Learned Control in Radio Access Networks (2026)
- Towards Operator-Empowered Vulnerability Hotfixing for 5G Radio Access Networks (2026)
- ToolMinimize: Auditing and Rewriting LLM Agent Tool Calls to Minimize Privacy Exposure (2026)
- Adaptive Traffic Camouflage: Causal and Resource-Aware Defense Against IoT Fingerprinting (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Get this paper in your agent:
hf papers read 2607.08809 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper