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arxiv: 2605.30791 · v1 · pith:YXSTVLYKnew · submitted 2026-05-29 · 💻 cs.NI

Where's Waldo Library? Using Reverse IP Geolocation to Identify Library IPs

Nishant Acharya , Anyu Yang , Humaira Fasih Ahmed Hashmi , Kevin Vermeulen , Shivani Kalamadi , Jiayi Liu , Ashutosh Kshirsagar , Elizabeth Belding
show 2 more authors
David Choffnes Alexander Gamero-Garrido
This is my paper

Pith reviewed 2026-06-28 20:29 UTC · model grok-4.3

classification 💻 cs.NI
keywords reverse IP geolocationpublic librariesIP address mappingbroadband measurementcommunity anchor institutionsDNS PTR recordsWHOIS validation
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The pith

Reverse IP geolocation maps roughly half of US public libraries to their IP prefixes from street addresses

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

The paper develops Reverse IP Geolocation to discover the IP addresses used by public libraries by starting from their known physical addresses rather than searching forward from IPs. Commercial geolocation databases generate candidate IP sets for each library location; these candidates are then narrowed using DNS PTR records, WHOIS registrations, broadband provider data, and active measurements. The evaluation reports successful mapping for about half of libraries, with the identified prefixes distributed across every US state and both urban and rural settings. This matters because libraries serve as key internet access points for underserved communities, so knowing their IPs at scale makes it possible to measure service quality remotely without contacting each site. The work focuses on libraries as a test case but frames the technique as a foundation for evaluating other community anchor institutions.

Core claim

The authors show that combining IP geolocation databases with library street addresses yields candidate IP sets that can be refined with PTR records, WHOIS registrations, provider data, and active probes to correctly identify the IP prefixes serving libraries in approximately half of cases, with geographic coverage spanning all US states and both urban and rural settings.

What carries the argument

Reverse IP Geolocation, which starts from known physical addresses to locate matching IP ranges in geolocation databases and validates the assignment to the specific institution using DNS, WHOIS, and measurements.

If this is right

  • Scalable remote measurement of internet service quality at libraries across the US becomes possible without individual site cooperation.
  • Policy evaluation of broadband access for unserved communities gains a new source of data based on library connectivity.
  • The same address-to-IP mapping process can be applied to other community anchor institutions that have publicly known addresses.
  • Repeated runs of the method can track changes in which IP prefixes are assigned to libraries over time.

Where Pith is reading between the lines

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

  • The technique could be tested on schools or community centers if their address lists are similarly public and complete.
  • Higher accuracy in commercial geolocation databases would directly raise the fraction of libraries that can be mapped.
  • Once the IPs are known, the same addresses could support ongoing passive monitoring of performance at these sites.

Load-bearing premise

Commercial geolocation databases produce candidate IP sets that are sufficiently accurate and complete to allow reliable identification of IPs serving specific known physical addresses of libraries.

What would settle it

A validation study that checks the candidate IPs against independent records or active probes and finds correct library matches in substantially fewer than half the cases would falsify the reported success rate.

Figures

Figures reproduced from arXiv: 2605.30791 by Alexander Gamero-Garrido, Anyu Yang, Ashutosh Kshirsagar, David Choffnes, Elizabeth Belding, Humaira Fasih Ahmed Hashmi, Jiayi Liu, Kevin Vermeulen, Nishant Acharya, Shivani Kalamadi.

Figure 1
Figure 1. Figure 1: Overview of Reverse IP Geolocation. The input is a dataset of library names and street addresses. RG [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Share of US ur￾ban and rural libraries vs. high-confidence IP identi￾fied libraries [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Number high confidence libraries (y-axis) hav [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The relationship between having near or far [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The number of candidate IP prefixes (x-axis, [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

Community anchor institutions (CAIs), such as libraries, schools, and community centers, are critical for providing Internet access to un- or under-served individuals and communities. Because many of these institutions are themselves under-provisioned, analyzing the reliability and quality of their Internet service is important. Doing so at scale requires knowing the IP addresses of these institutions so that broadband measurement and policy evaluation can occur. Unfortunately, these IPs are not systematically documented. As a first step towards widespread, scalable evaluation of CAI Internet connectivity, this paper presents Reverse IP Geolocation (RG), a new framework to infer IP addresses from physical address data. A key insight is that CAI street addresses are publicly known, which allows us to identify a candidate set of IPs from commercial geolocation that are likely serving the location associated with a CAI. In this paper, \textbf{we focus on US public libraries}, which offer both geographic diversity across thousands of locations, and some publicly available institutional records (\eg{}WHOIS registrations) that enable systematic validation of our approach. Our approach offers a novel integration of IP geolocation databases, DNS PTR records, WHOIS registrations, broadband provider data, and active measurements to identify IPs likely assigned to libraries and validate them. Based on evaluations, our approach can map a library to its IP prefix approx. half of the time, with coverage across all US states, as well as urban and rural areas. Our results highlight the feasibility of mapping CAI presence in IP space and offer a foundation for large-scale, remote broadband infrastructure evaluation.

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 / 1 minor

Summary. The paper introduces Reverse IP Geolocation (RG), a framework to infer IP addresses of US public libraries from known physical street addresses. It queries commercial geolocation databases to generate candidate IP sets, then integrates DNS PTR records, WHOIS registrations, broadband provider data, and active measurements to identify and validate library-assigned IPs. The central claim is that this maps a library to its IP prefix approximately half the time, with coverage across all US states and both urban and rural areas.

Significance. If the results hold, the work offers a scalable approach to identify IP addresses for community anchor institutions, enabling large-scale remote evaluation of their broadband connectivity and supporting policy analysis in underserved regions. The integration of multiple data sources and the choice of libraries (with available WHOIS records for validation) are appropriate strengths; the claimed geographic breadth adds to potential utility.

major comments (2)
  1. [Abstract and evaluation section] Abstract and evaluation section: The claim of an approximately 50% mapping success rate is presented without details on the total number of libraries considered, the criteria for selecting candidates from commercial geolocation databases, the validation methodology against ground truth, error analysis, or dataset characteristics. This prevents verification of the data-to-claim linkage and is load-bearing for the paper's primary contribution.
  2. [Framework description (likely §3)] Framework description (likely §3): The method begins by querying commercial geolocation databases with library addresses to produce candidate IP sets. No analysis, quantification, or discussion is provided of the accuracy, completeness, or error rates of these databases for library locations (e.g., due to coarse granularity or outdated records). This assumption is load-bearing because the subsequent DNS/WHOIS/active measurement steps cannot recover correct mappings if the true library IPs are absent from the initial candidate sets.
minor comments (1)
  1. [Abstract] The abstract uses the acronym CAI without spelling it out on first use, though it is expanded later in the text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and the opportunity to clarify and strengthen the manuscript. We address each major comment below, agreeing that additional details are needed for verifiability, and commit to revisions that directly incorporate the requested information without altering the core claims or methodology.

read point-by-point responses

  1. Referee: [Abstract and evaluation section] Abstract and evaluation section: The claim of an approximately 50% mapping success rate is presented without details on the total number of libraries considered, the criteria for selecting candidates from commercial geolocation databases, the validation methodology against ground truth, error analysis, or dataset characteristics. This prevents verification of the data-to-claim linkage and is load-bearing for the paper's primary contribution.

    Authors: We agree that the abstract and evaluation section require expanded details to support verification of the ~50% success rate. In the revised manuscript, we will update the abstract and add a dedicated evaluation subsection specifying: the total number of libraries considered (all US public libraries with publicly listed street addresses), candidate selection criteria from commercial geolocation databases (IPs within a fixed radius of the address, ranked by proximity), the validation methodology (cross-check against WHOIS records for libraries with documented institutional ownership as ground truth), a summary error analysis (false positive/negative rates from the multi-source filtering), and dataset characteristics (geographic distribution across states and urban/rural splits). This directly addresses the data-to-claim linkage. revision: yes

  2. Referee: [Framework description (likely §3)] Framework description (likely §3): The method begins by querying commercial geolocation databases with library addresses to produce candidate IP sets. No analysis, quantification, or discussion is provided of the accuracy, completeness, or error rates of these databases for library locations (e.g., due to coarse granularity or outdated records). This assumption is load-bearing because the subsequent DNS/WHOIS/active measurement steps cannot recover correct mappings if the true library IPs are absent from the initial candidate sets.

    Authors: We acknowledge the gap in analyzing the commercial geolocation databases' accuracy, completeness, and error rates for library locations, which is a load-bearing assumption. In the revised framework description (§3), we will add a new subsection quantifying database performance on our library address set (e.g., fraction of libraries yielding at least one candidate IP, precision estimates from validated cases), discussing error sources such as coarse granularity (particularly in rural areas) and outdated records, and explaining the impact on downstream steps. We will also note limitations where true IPs may be absent from candidates and how the integration of DNS PTR, WHOIS, and active measurements provides partial mitigation. revision: yes

Circularity Check

0 steps flagged

No circularity; method integrates external commercial databases and records without self-referential definitions or fitted predictions.

full rationale

The paper presents a framework (RG) that queries commercial geolocation databases with known library street addresses to generate candidate IP sets, then integrates DNS PTR, WHOIS, broadband provider data, and active measurements for validation. No equations, parameters fitted to subsets of data, or predictions that reduce to inputs by construction appear. Central claim (mapping ~50% of libraries to IP prefixes with broad coverage) rests on the external accuracy of commercial geo DBs rather than any self-definition or self-citation chain. This is a standard data-integration approach whose validity is falsifiable against ground-truth library records outside the paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the accuracy of external commercial geolocation databases as an unverified input; no free parameters, new entities, or additional axioms are introduced in the abstract.

axioms (1)
  • domain assumption Commercial IP geolocation databases produce candidate IP sets that are sufficiently accurate and complete to allow reliable identification of IPs serving specific known physical addresses of libraries.
    This premise is required to generate the initial candidate sets from street addresses and is not supported by evidence within the abstract.

pith-pipeline@v0.9.1-grok · 5859 in / 1174 out tokens · 25418 ms · 2026-06-28T20:29:13.747029+00:00 · methodology

discussion (0)

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