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arxiv: 2605.10812 · v1 · submitted 2026-05-11 · 💻 cs.NI · cs.CR· cs.CY

Recognition: 2 theorem links

· Lean Theorem

Democratizing Measurement of Critical Mobile Infrastructure: Security and Privacy in an Increasingly Centralized Communication Ecosystem

Gabriel K. Gegenhuber
Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:06 UTC · model grok-4.3

classification 💻 cs.NI cs.CRcs.CY
keywords mobile networksmeasurement platformsnetwork securityprivacycellular infrastructureOTT messagingopen source toolsVoWiFi
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The pith

Open-source platforms enable independent measurements of mobile network security and privacy without operator cooperation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Cellular networks have grown more complex through technologies like VoWiFi, eSIMs, roaming arrangements, and the shift to OTT messaging services that bypass traditional operator channels. This work establishes methods for independent, scalable, and reproducible measurements of these systems without needing any cooperation from operators or platform providers. It achieves this by designing, building, and releasing open-source measurement platforms that support controlled experiments on radio networks, operator services, and messaging applications. A sympathetic reader would care because such tools could expose security and privacy issues in the everyday communication infrastructure that millions rely on, especially in areas with few alternatives.

Core claim

This dissertation shows that new approaches for independent, scalable, and reproducible measurements of mobile communication systems are possible without requiring cooperation from network or platform operators. The key is designing, implementing, and open-sourcing measurement platforms that allow controlled experiments across cellular radio networks, operator-provided services, and OTT messaging applications.

What carries the argument

Open-source measurement platforms that enable controlled experiments across cellular radio networks, operator-provided services, and OTT messaging applications.

If this is right

  • Researchers gain the ability to run controlled experiments on cellular radio networks without operator approval.
  • Reproducible studies of security and privacy in roaming, virtual operators, and zero-rating become feasible.
  • Analysis of how OTT services like WhatsApp and Signal bypass operator channels can be conducted at scale.
  • Geographically diverse measurements of mobile infrastructure are now more accessible to independent parties.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Widespread use of these tools could pressure operators to improve transparency on their own.
  • The platforms might be extended to test emerging features such as 5G slicing or additional VoWiFi variants.
  • Open availability of the code enables community-driven additions for new regions or services.

Load-bearing premise

Independent, scalable measurements can be performed effectively without operator cooperation and will yield reproducible insights into security and privacy.

What would settle it

Deploying the open-sourced platforms in multiple locations and observing that they cannot reliably access or measure the targeted network features, or that results fail to reproduce consistently across trials.

Figures

Figures reproduced from arXiv: 2605.10812 by Gabriel K. Gegenhuber.

Figure 1
Figure 1. Figure 1: illustrates the three complementary research perspectives considered [PITH_FULL_IMAGE:figures/full_fig_p016_1.png] view at source ↗
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p017_1.png] view at source ↗
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p018_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: ). We leverage these alternative access technologies by conducting Internet [PITH_FULL_IMAGE:figures/full_fig_p019_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: Simplified technology neutral structure of a cellular 3GPP network with roaming, and [PITH_FULL_IMAGE:figures/full_fig_p029_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Traditional approach with poor scalability: Every [PITH_FULL_IMAGE:figures/full_fig_p031_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: High-level overview of MOBILEATLAS compo￾nents: SIMs connected to our SIM Providers can be virtually connected to any measurement probe at the target location. 5.5 Ethical Considerations We consider ethical aspects for three roles: The designer of the method, the operator of the testbed and measurement network, and the researcher that executes tests and measurements. Not all of the following apply to all o… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of ringback spectra. 2 Relaying SIM Communication for Cellular Network Measurements 26 [PITH_FULL_IMAGE:figures/full_fig_p038_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Fingerprinting ringback tones (without VoLTE). [PITH_FULL_IMAGE:figures/full_fig_p039_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Left: first prototype; right: current version. [PITH_FULL_IMAGE:figures/full_fig_p043_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Wiring diagram between the SoC and the modem. [PITH_FULL_IMAGE:figures/full_fig_p044_9.png] view at source ↗
Figure 8
Figure 8. Figure 8: Probe architecture: GPIO ports physically emulate [PITH_FULL_IMAGE:figures/full_fig_p044_8.png] view at source ↗
Figure 1
Figure 1. Figure 1: Architecture and components of the MOBILEATLAS measurement platform into three main components: SIM providers that allow sharing SIM card access, measurement probes that act as a local breakout to the cellular network, and a management server that connects the prior two components and acts as command and control unit for the measurement probes. To execute a measurement, any SIM card that is attached to our… view at source ↗
Figure 2
Figure 2. Figure 2: Involved actors and traffic flow when verifying zero-rated data traffic [PITH_FULL_IMAGE:figures/full_fig_p051_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Actors and traffic flow when checking for IP-based classification [PITH_FULL_IMAGE:figures/full_fig_p051_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Involved actors and traffic flow when checking for hostname-based [PITH_FULL_IMAGE:figures/full_fig_p052_4.png] view at source ↗
Figure 1
Figure 1. Figure 1: An Indian operator states in the FAQs [2] that [PITH_FULL_IMAGE:figures/full_fig_p072_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (Simplified) LTE network architecture for VoLTE [PITH_FULL_IMAGE:figures/full_fig_p073_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: A containerized architecture isolates independent [PITH_FULL_IMAGE:figures/full_fig_p074_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Summarized results or our DNS discovery and IKE [PITH_FULL_IMAGE:figures/full_fig_p078_6.png] view at source ↗
Figure 1
Figure 1. Figure 1: VoLTE compared to VoWiFi over an untrusted In [PITH_FULL_IMAGE:figures/full_fig_p086_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: VoWiFi uses multiple tunnels to ensure security: [PITH_FULL_IMAGE:figures/full_fig_p087_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Development of the IPsec IKE Profile as defined [PITH_FULL_IMAGE:figures/full_fig_p088_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Number of MNOs per supported DH group (client [PITH_FULL_IMAGE:figures/full_fig_p092_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Operator-specific configuration of rekey timings. [PITH_FULL_IMAGE:figures/full_fig_p093_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Number of MNOs per supported DH group 6 Active MNO-side ePDG Scanning 6.1 Implementation To analyze operators’ IKE handshakes and probe different key exchange methods we operated as follows. First, we queried all possible ePDG DNS names (Section 2.2) with massdns10 , delegating all queries to a local unbound11 instance, itera￾tively resolving DNS requests (i.e., getting the IP addresses from the authoritat… view at source ↗
Figure 9
Figure 9. Figure 9: An ePDG server can switch from the initially se [PITH_FULL_IMAGE:figures/full_fig_p096_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Globally Static Set of DH Keys: Remediation over Time [PITH_FULL_IMAGE:figures/full_fig_p103_11.png] view at source ↗
Figure 1
Figure 1. Figure 1: Round-trip times (RTT) of delivery receipts, which are [PITH_FULL_IMAGE:figures/full_fig_p106_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Simplified depiction of client-fanout for Multi-Device [PITH_FULL_IMAGE:figures/full_fig_p107_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A device’s online status can be consistently and [PITH_FULL_IMAGE:figures/full_fig_p111_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: WhatsApp Use: RTTs are 350 ms if the application is [PITH_FULL_IMAGE:figures/full_fig_p112_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Characteristic screen on/off timings for different manufacturers and chipsets (all measured as [PITH_FULL_IMAGE:figures/full_fig_p113_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Real-World Tracking Scenario of two companion devices (web-client and native client) and the main device (smartphone) [PITH_FULL_IMAGE:figures/full_fig_p114_8.png] view at source ↗
Figure 8
Figure 8. Figure 8: The sending device did not have any prior relation [PITH_FULL_IMAGE:figures/full_fig_p115_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Whats App Message Flow as discovered by Schnitzler [PITH_FULL_IMAGE:figures/full_fig_p120_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: For the MediaTek-based Xiaomi Poco M3 Pro 5G, [PITH_FULL_IMAGE:figures/full_fig_p121_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: For the Samsung Galaxy S23, we needed to lower [PITH_FULL_IMAGE:figures/full_fig_p121_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of different probing intervals (2 s, 20 s), scenarios ( [PITH_FULL_IMAGE:figures/full_fig_p122_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Comparison of different probing intervals (2 s, 20 s), scenarios ( [PITH_FULL_IMAGE:figures/full_fig_p123_14.png] view at source ↗
Figure 1
Figure 1. Figure 1: High-level overview of the intended prekey bundle [PITH_FULL_IMAGE:figures/full_fig_p128_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: High-level overview of the attack. Eve is flooding [PITH_FULL_IMAGE:figures/full_fig_p132_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Each device’s online status can be independently, [PITH_FULL_IMAGE:figures/full_fig_p133_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Characteristic refill behavior of different smartphone models in various device and connection states. Time where no [PITH_FULL_IMAGE:figures/full_fig_p141_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Signal protocol layout, when no ephemeral (one-time) prekeys are available on the server. Identical keys a highlighted with the same color. 8 Exploits via E2EE Prekeying Mechanism on Instant Messengers 132 [PITH_FULL_IMAGE:figures/full_fig_p144_5.png] view at source ↗
Figure 9
Figure 9. Figure 9 [PITH_FULL_IMAGE:figures/full_fig_p146_9.png] view at source ↗
read the original abstract

Cellular networks serve as the backbone of global communication, providing critical access to telephony and the Internet, often in regions lacking alternatives. However, the growing complexity of these networks, driven by architectural innovations (e.g., Voice over IP, eSIMs) and commercial dynamics (e.g., roaming, virtual operators, zero-rating), remains poorly understood due to the lack of open, scalable, and geographically diverse measurement tools and independent measurement studies. Moreover, access to mobile networks today is no longer limited to the traditional radio interface. Technologies like Voice-over-WiFi (VoWiFi) offer alternative connectivity paths via third-party Internet infrastructure, extending operator reach into environments with limited cellular coverage. At the same time, over-the-top (OTT) messaging services such as WhatsApp and Signal have become central to modern communication, accounting for a substantial share of global messaging and voice traffic while bypassing traditional operator-controlled channels entirely. This dissertation addresses these challenges by introducing new approaches for independent, scalable, and reproducible measurements of mobile communication systems without requiring cooperation from network or platform operators. We design, implement, and open-source measurement platforms that enable controlled experiments across cellular radio networks, operator-provided services, and OTT messaging applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript claims that the growing complexity of cellular networks (driven by VoIP, eSIMs, roaming, VoWiFi, and OTT services such as WhatsApp and Signal) is poorly understood due to the absence of open, scalable measurement tools. It addresses this by designing, implementing, and open-sourcing measurement platforms that enable independent, controlled, scalable, and reproducible experiments on cellular radio networks, operator-provided services, and OTT messaging applications without requiring cooperation from network or platform operators.

Significance. If the platforms deliver the claimed controlled experiments and reproducible insights, the work would be significant for the field of mobile network measurement. It would lower barriers to independent research on security and privacy in an increasingly centralized ecosystem, complementing operator-internal studies with geographically diverse, open data. The explicit open-sourcing of the platforms is a concrete strength that supports community validation and extension.

major comments (2)
  1. [Abstract and experimental evaluation sections] The central claim that the platforms 'enable controlled experiments' across cellular radio networks rests on the unverified assumption that cellular variability (signal strength, cell load, roaming state, eSIM provisioning, VoWiFi handoff) can be isolated without operator cooperation. No quantitative reproducibility metrics (e.g., variance across repeated trials, operators, or locations) are referenced to substantiate this; this is load-bearing for the reproducibility and scalability assertions.
  2. [Methods and evaluation chapters] The manuscript does not appear to include statistical controls or instrumentation details that would allow isolation of operator-specific behaviors from environmental confounders. Without such controls, the assertion that measurements are 'controlled' and yield reproducible security/privacy insights cannot be evaluated.
minor comments (2)
  1. [Introduction] Clarify the exact scope of 'OTT messaging applications' covered and whether the platforms include any baseline comparisons against existing tools (e.g., prior cellular measurement frameworks).
  2. [Software release section] Ensure all open-sourced components are accompanied by build instructions and example datasets so that independent reproduction is immediately feasible.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. The comments highlight important areas where we can strengthen the presentation of our measurement platforms' capabilities for controlled and reproducible experiments. We address each major comment below and describe the planned revisions.

read point-by-point responses

  1. Referee: [Abstract and experimental evaluation sections] The central claim that the platforms 'enable controlled experiments' across cellular radio networks rests on the unverified assumption that cellular variability (signal strength, cell load, roaming state, eSIM provisioning, VoWiFi handoff) can be isolated without operator cooperation. No quantitative reproducibility metrics (e.g., variance across repeated trials, operators, or locations) are referenced to substantiate this; this is load-bearing for the reproducibility and scalability assertions.

    Authors: We agree that the current manuscript does not include quantitative reproducibility metrics such as variance across repeated trials. The platforms enable controlled experiments primarily through open-source scripting of device configurations, automated test execution, and detailed state logging (e.g., signal strength and roaming indicators), which standardize the measurement process across runs. In the revised manuscript, we will add a new subsection in the evaluation chapter presenting results from repeated trials, including standard deviations and comparisons across locations and operators. We will also explicitly discuss the limitations of isolating variables like cell load without operator cooperation, clarifying that the tools support reproducible methodology rather than perfect environmental isolation. revision: yes

  2. Referee: [Methods and evaluation chapters] The manuscript does not appear to include statistical controls or instrumentation details that would allow isolation of operator-specific behaviors from environmental confounders. Without such controls, the assertion that measurements are 'controlled' and yield reproducible security/privacy insights cannot be evaluated.

    Authors: The methods chapter currently describes the core instrumentation, including APIs for network state capture and logging mechanisms. To address this, we will expand it with additional details on how we mitigate confounders, such as repeating experiments at varied times of day, using multiple device models, and logging environmental variables for post-hoc analysis. We will also include a discussion of statistical controls employed in the presented case studies. These additions will allow readers to better evaluate the degree of control and reproducibility achievable with the platforms. revision: yes

Circularity Check

0 steps flagged

No circularity: tool-building paper with no derivations or self-referential reductions

full rationale

The paper's core contribution is the design, implementation, and open-sourcing of measurement platforms for cellular networks, operator services, and OTT apps, enabling independent experiments without operator cooperation. No equations, fitted parameters, predictions, or derivation chains appear in the provided text. The claims are empirical and implementation-focused rather than mathematical, so no step reduces to its own inputs by construction, self-citation, or renaming. External validation is possible via the released platforms and experiments, keeping the work self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no equations, parameters, or technical derivations visible. No free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5518 in / 934 out tokens · 13855 ms · 2026-05-12T04:06:34.513366+00:00 · methodology

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Reference graph

Works this paper leans on

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