Quantifying the Privacy Posture of Operator-Side 5G/O-RAN Profiles

2026-10-06

AI Summary

This study addresses re-identification risks in anonymized 5G/O-RAN network configurations by constructing a privacy quantification framework. Methodologically, data are collected via passive monitoring and rule-based deep packet inspection, followed by generalization and suppression using k-anonymity, l-diversity, and t-closeness, with transformation effects visualized through a dashboard. The core contribution is a proposed mean-class-size variant of the Prosecution Privacy Indicator (PPI), which demonstrates strong correlation with attack metrics and outperforms the traditional minimum-k metric. Furthermore, this work reveals that pseudonymization mechanisms are ineffective, whereas generalization significantly mitigates risk, though high suppression rates compromise minority-class recall. These findings address the inability of standard PPI to reflect true security boundaries.

Abstract

Operator-side network profiles derived from 5G/ORAN traffic carry personal data such as ephemeral subscriber identifiers, slice-level KPIs, and control-plane signalling, and must be anonymised before release to a federated-learning aggregator, threat-intelligence exchange, or ML training pipeline. We study how much re-identification risk remains after standard operator-side anonymisation. We quantify privacy posture with k-anonymity, l-diversity and t-closeness, aggregate them into a composite Privacy-Posture Index (PPI), and measure residual re-identification across eight transformation configurations on internal PCAP captures and the public Idaho Labs 5GAD corpus, under a full-QI syntactic bound and two simulated adversaries. The evaluation is modest in scale, and we read its trends as indicative rather than definitive. Three findings emerge. Pseudonymisation alone leaves re-identification unchanged; material privacy gains arise when quasi-identifiers are coarsened through generalisation, optionally combined with suppression. A downstream classification task then shows that suppression-heavy releases retain majority-class utility but sacrifice much of their minority-class recall, a cost the aggregate metrics hide. Finally, the standard kmin-based PPI correlates only modestly with the disclosure bound and not at all with the partial-knowledge attack, whereas a mean-class-size variant PPI correlates strongly with all three disclosure/attack measures; we therefore read PPI as a regulator-facing summary, not a security bound. The profiles are produced by passive operator-side monitoring with rule-based DPI; our contribution is the privacy-quantification layer that computes these metrics, applies the transformation policy, and exposes both through an inspectable dashboard.

Problem

• 5G/O-RAN
• Privacy Quantification
• Re-identification Risk
• Anonymization
• Network Profiles

Innovation

• Privacy-Posture Index
• 5G/O-RAN
• Re-identification Risk
• Anonymization
• Utility-Privacy Trade-off