pith. sign in

arxiv: 2605.30692 · v1 · pith:UCND3UGKnew · submitted 2026-05-29 · 💻 cs.NI

Not All Roads Lead to Rome: How VPN Selection Alters What We Measure and Infer about Web Infrastructure

Sachin Kumar Singh , Robert Ricci , Alexander Gamero-Garrido This is my paper

Pith reviewed 2026-06-28 20:34 UTC · model grok-4.3

classification 💻 cs.NI
keywords VPN measurementsweb infrastructureDNS resolutionCDN replica selectionvantage pointsnetwork measurementpeering paths
0
0 comments X

The pith

Different VPN providers in the same country produce inconsistent measurements of web endpoint locations, hosting, and replicas.

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

Web measurement studies commonly treat any commercial VPN in a given country as an interchangeable vantage point. This paper shows that the choice of VPN provider leads to materially different conclusions about where endpoints are located, which organizations host them, and which physical replicas serve requests. The differences originate primarily below the client in name resolution and replica selection, where VPNs run their own in-country DNS infrastructure that intercepts queries, CDNs steer identical queries toward different replicas based on the exit network, and peering paths deliver the same DNS answers to distinct physical facilities. The evidence comes from large-scale browser-based measurements across fourteen countries using four major VPN providers, together with targeted DNS and replica-selection probes. The authors distill the results into recommended reporting practices for VPN-based web measurements.

Core claim

Commercial VPNs cannot be treated as interchangeable vantage points within a country. The same country measured through different providers yields different conclusions about endpoint locations, hosting entities, and physical replicas. This variability is driven primarily by layers below the client: commercial VPN providers operate their own in-country DNS infrastructure that often intercepts queries regardless of client configuration; CDNs steer on the exit network, sending identical queries to different replicas; and peering paths route identical DNS answers to different physical facilities.

What carries the argument

Variability across the vantage identity, name resolution, and replica selection layers of the VPN-to-endpoint path.

If this is right

  • Web infrastructure studies that rely on a single VPN per country may reach incomplete or biased conclusions about endpoint distribution and hosting.
  • DNS resolution for web queries is frequently controlled by the VPN provider's own infrastructure rather than client settings.
  • CDN replica selection depends on the specific exit network of the chosen VPN.
  • Peering paths can direct identical DNS responses to different physical facilities.
  • Measurement reports should document the specific VPN providers used and consider testing multiple providers.

Where Pith is reading between the lines

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

  • Studies of regional web performance or censorship that use only one VPN per country may systematically understate or overstate certain infrastructure patterns.
  • Discrepancies between independent measurement campaigns of the same region could partly trace to unstated differences in VPN choice.
  • Direct connections or non-VPN vantage points might be needed to cross-check results when precision about physical replica placement matters.

Load-bearing premise

The large-scale browser-based measurements and targeted probes accurately capture the contributions of vantage identity, name resolution, and replica selection without confounding effects from other parts of the network path.

What would settle it

Finding that different VPN providers in the same country consistently return the same endpoint locations, hosting providers, and replica selections for the same queries would falsify the claim.

Figures

Figures reproduced from arXiv: 2605.30692 by Alexander Gamero-Garrido, Robert Ricci, Sachin Kumar Singh.

Figure 1
Figure 1. Figure 1: Top-10 destination-countries by VPN, for a representative subset of seven countries. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pair-wise Jaccard similarity of the set of hosting organizations reached across VPNs, averaged across all [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Top-10 destination-country distribution of contacted endpoints by VPN, for all fourteen source countries. [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pair-wise Jensen–Shannon distance between per-VPN destination-country distributions, for each source [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
read the original abstract

Web-measurement studies treat commercial VPNs as interchangeable vantage points within a country, assuming that any VPN in a particular country is as good as any other. We show that this assumption does not hold: the same country measured through different VPN providers yields materially different conclusions about where endpoints sit, who hosts them, and which physical replicas serve them. Using large-scale browser-based measurements across fourteen countries and four major VPN providers, complemented by targeted DNS and replica-selection probes, we examine sources of this variability across three layers of the VPN-to-endpoint path: vantage identity, name resolution, and replica selection. We find that the variability is driven primarily by layers below the client: commercial VPN providers operate their own in-country DNS infrastructure, often intercepting queries regardless of client configuration; CDNs steer on the exit network, sending identical queries to different replicas; and peering paths route identical DNS answers to different physical facilities. We distill these findings into a set of reporting practices for VPN-based Web measurement.

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

1 major / 2 minor

Summary. The paper claims that commercial VPNs cannot be treated as interchangeable vantage points within the same country for web measurements. Large-scale browser-based measurements across 14 countries and 4 VPN providers, supplemented by targeted DNS and replica-selection probes, show that the same country yields materially different inferences about endpoint locations, hosting, and physical replicas. The authors decompose the variability into three layers (vantage identity, name resolution via VPN DNS interception, and replica selection via CDN exit-network steering and peering paths) and distill the results into recommended reporting practices.

Significance. If the empirical findings hold, the work has clear significance for the network measurement community by demonstrating that VPN choice systematically alters infrastructure inferences. The three-layer decomposition provides a concrete framework for localizing sources of variability, and the combination of browser-based measurements with targeted probes offers a replicable approach. This directly addresses a widespread methodological assumption and supplies actionable guidance for future studies.

major comments (1)
  1. [Abstract/Methodology] Abstract and methodology description: the claim that differences are driven by the three layers rests on the premise that browser measurements and probes isolate vantage identity, name resolution, and replica selection without confounding path effects, yet no quantitative details are provided on probe volume, statistical tests for cross-VPN differences, or controls for other network variables; this detail is load-bearing for attributing observed variability specifically to the named layers.
minor comments (2)
  1. The selection criteria for the four VPN providers and fourteen countries are not stated; adding this information would strengthen replicability without altering the central claims.
  2. Consider including a table summarizing per-country, per-VPN differences in inferred hosting and replica locations to make the scale of the effect more immediately visible.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation of minor revision. We address the single major comment below and will incorporate the requested clarifications into the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract/Methodology] Abstract and methodology description: the claim that differences are driven by the three layers rests on the premise that browser measurements and probes isolate vantage identity, name resolution, and replica selection without confounding path effects, yet no quantitative details are provided on probe volume, statistical tests for cross-VPN differences, or controls for other network variables; this detail is load-bearing for attributing observed variability specifically to the named layers.

    Authors: We agree that the abstract and methodology description would be strengthened by explicit quantitative details on how the three layers are isolated. The current text describes the browser-based measurements and targeted probes at a high level but does not report probe volumes, the specific statistical tests used, or the controls applied for path effects. In the revision we will add a dedicated paragraph (or subsection) in the methodology that states the total number of browser measurements and targeted probes performed, the statistical procedures (including any tests for cross-VPN differences), and the controls employed (such as fixed client resolver settings, latency measurements, and path tracing) to limit confounding. This addition will make the attribution to vantage identity, name resolution, and replica selection more transparent and reproducible. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an empirical measurement study using browser-based tests and targeted DNS/replica probes across 14 countries and 4 VPN providers. No equations, derivations, fitted parameters, or predictions are introduced; all claims rest on direct observation of differences in vantage identity, name resolution, and replica selection. The methodology is constructed to isolate these layers without reducing any result to a self-definition, self-citation chain, or input-by-construction. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract describes an empirical study without introducing new mathematical parameters, unproven axioms, or new entities.

axioms (1)
  • domain assumption Standard assumptions in network measurement studies hold, such as that browser-based probes reflect real user experiences.
    Implicit in the use of browser-based measurements.

pith-pipeline@v0.9.1-grok · 5710 in / 1276 out tokens · 38406 ms · 2026-06-28T20:34:36.208397+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

88 extracted references · 8 canonical work pages · 4 internal anchors

  1. [1]

    Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation)

    2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation). Official Journal of the European Union L 119. https://eur-lex.europa .eu/eli/reg/2016/679/oj Accessed: 2025-10-01

    2016

  2. [2]

    Regulation (EU) 2022/2065 on a Single Market for Digital Services (Digital Services Act)

    2022. Regulation (EU) 2022/2065 on a Single Market for Digital Services (Digital Services Act). Official Journal of the European Union L 277. https://eur-lex.europa.eu/eli/reg/2022/2065/oj Accessed: 2025-10-01

    2022

  3. [3]

    Sadia Afroz, Michael Carl Tschantz, Shaarif Sajid, Shoaib Asif Qazi, Mobin Javed, and Vern Paxson. 2018. Exploring server-side blocking of regions.arXiv preprint arXiv:1805.11606(2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  4. [4]

    Ahrefs. 2025. Ahrefs-Marketing Intelligence Tools Powered by Big Data. https://ahrefs.com. Accessed: 2025-07-21

    2025

  5. [5]

    Waqar Aqeel, Balakrishnan Chandrasekaran, Anja Feldmann, and Bruce M Maggs. 2020. On landing and internal web pages: The strange case of jekyll and hyde in web performance measurement. InProceed- ings of the ACM Internet Measurement Conference. 680–695

    2020

  6. [6]

    RIPE Atlas. [n. d.]. RIPE NCC’s main Internet data measurement system. https://atlas.ripe.net/. Accessed 2025-05-05

    2025

  7. [7]

    Vaibhav Bajpai, Steffie Jacob Eravuchira, and Jürgen Schönwälder

  8. [8]

    Lessons learned from using the ripe atlas platform for measure- ment research.ACM SIGCOMM Computer Communication Review45, 3 (2015), 35–42

    2015

  9. [9]

    Timothy Barron and Nick Nikiforakis. 2017. Picky attackers: Quantify- ing the role of system properties on intruder behavior. InProceedings of the 33rd annual computer security applications conference. 387–398

    2017

  10. [10]

    Ignacio Bermudez, Stefano Traverso, Marco Mellia, and Maurizio Mu- nafo. 2013. Exploring the cloud from passive measurements: The Amazon AWS case. In2013 Proceedings IEEE INFOCOM. IEEE, 230– 234

    2013

  11. [11]

    2024.Measuring and Understanding TTL Violations in DNS Resolvers

    Protick Bhowmick. 2024.Measuring and Understanding TTL Violations in DNS Resolvers. Ph. D. Dissertation. Virginia Tech

    2024

  12. [12]

    Bright Data. 2025. Bright Data (formerly Luminati). https://brightda ta.com. Accessed: 2025-07-29

    2025

  13. [13]

    Michael Butkiewicz, Harsha V Madhyastha, and Vyas Sekar. 2011. Understanding website complexity: measurements, metrics, and im- plications. InProceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. 313–328

    2011

  14. [14]

    CAIDA. [n. d.]. Archipelago (Ark): CAIDA’s active measurement infrastructure serving the network research. https://www.caida.org/ projects/ark/locations/. Accessed 2025-05-05

    2025

  15. [15]

    CAIDA. 2014. The CAIDA UCSD AS to Organization Mapping Dataset. https://www.caida.org/catalog/datasets/as-organizations/

    2014

  16. [16]

    Patricia Callejo, Marco Gramaglia, Ruben Cuevas, and Angel Cuevas

  17. [17]

    A deep dive into the accuracy of IP Geolocation Databases and its impact on online advertising.IEEE Transactions on Mobile Computing22, 8 (2022), 4359–4373

    2022

  18. [18]

    Darion Cassel, Su-Chin Lin, Alessio Buraggina, William Wang, An- drew Zhang, Lujo Bauer, Hsu-Chun Hsiao, Limin Jia, and Timothy Libert. 2022. Omnicrawl: Comprehensive measurement of web track- ing with real desktop and mobile browsers.Proceedings on Privacy Enhancing Technologies(2022)

    2022

  19. [19]

    Rishabh Chhabra, Paul Murley, Deepak Kumar, Michael Bailey, and Gang Wang. 2021. Measuring DNS-over-HTTPS Performance around the World. InProceedings of the 21st ACM Internet Measurement Con- ference. 351–365

    2021

  20. [20]

    Jinchun Choi, Mohammed Abuhamad, Ahmed Abusnaina, Afsah An- war, Sultan Alshamrani, Jeman Park, Daehun Nyang, and David Mo- haisen. 2020. Understanding the proxy ecosystem: A comparative analysis of residential and open proxies on the internet.IEEE Access8 (2020), 111368–111380

    2020

  21. [21]

    2024.Overview of CrUX

    Chrome for Developers. 2024.Overview of CrUX. https://developer. chrome.com/docs/crux

    2024

  22. [22]

    Taejoong Chung, David Choffnes, and Alan Mislove. 2016. Tunneling for transparency: A large-scale analysis of end-to-end violations in the internet. InProceedings of the 2016 Internet Measurement Conference. 199–213

    2016

  23. [23]

    Danilo Cicalese, Jordan Augé, Diana Joumblatt, Timur Friedman, and Dario Rossi. 2015. Characterizing IPv4 anycast adoption and deploy- ment. InProceedings of the 11th ACM Conference on Emerging Network- ing Experiments and Technologies. 1–13

    2015

  24. [24]

    Cloudflare. 2025. Cloudflare Radar - Domain Rankings. https://radar. cloudflare.com/domains. Accessed: 2025-07-21

    2025

  25. [25]

    Carlo Contavalli, Wilmer van der Gaast, David C Lawrence, and Warren Kumari. 2016. Client Subnet in DNS Queries. RFC 7871. doi:10.17487/RFC7871

    work page doi:10.17487/rfc7871 2016

  26. [26]

    Casey Deccio, Jeff Sedayao, Krishna Kant, and Prasant Mohapatra

  27. [27]

    In2010 Proceedings IEEE INFOCOM

    Measuring availability in the domain name system. In2010 Proceedings IEEE INFOCOM. IEEE, 1–5

  28. [28]

    Martin Degeling, Christine Utz, Christopher Lentzsch, Henry Hosseini, Florian Schaub, and Thorsten Holz. 2018. We value your privacy... now take some cookies: Measuring the GDPR’s impact on web privacy. arXiv preprint arXiv:1808.05096(2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  29. [29]

    Trinh Viet Doan, Roland van Rijswijk-Deij, Oliver Hohlfeld, and Vaib- hav Bajpai. 2022. An empirical view on consolidation of the web.ACM Transactions on Internet Technology (TOIT)22, 3 (2022), 1–30

    2022

  30. [30]

    Dmitry Duplyakin, Robert Ricci, Aleksander Maricq, Gary Wong, Jonathon Duerig, Eric Eide, Leigh Stoller, Mike Hibler, David John- son, Kirk Webb, Aditya Akella, Kuangching Wang, Glenn Ricart, Larry Landweber, Chip Elliott, Michael Zink, Emmanuel Cecchet, Snigdhaswin Kar, and Prabodh Mishra. 2019. The Design and Op- eration of CloudLab. InProceedings of th...

    2019

  31. [31]

    Rob van Eijk, Hadi Asghari, Philipp Winter, and Arvind Narayanan

  32. [32]

    InWorkshop on Technology and Consumer Protection (ConPro’19)

    The impact of user location on cookie notices (inside and outside of the European union). InWorkshop on Technology and Consumer Protection (ConPro’19)

  33. [33]

    Alexander Gamero-Garrido, Kicho Yu, Sumukh Vasisht Shankar, Sachin Kumar Singh, Sindhya Balasubramanian, Alexander Wilcox, and David Choffnes. 2025. Empirically Measuring Data Localization in the EU. InProceedings on Privacy Enhancing Technologies (PoPETs)

    2025

  34. [34]

    Andreas Guillot, Romain Fontugne, Philipp Winter, Pascal Merindol, Alistair King, Alberto Dainotti, and Cristel Pelsser. 2019. Chocolatine: Outage detection for internet background radiation. In2019 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 1–8

    2019

  35. [35]

    Muhammad Yasir Muzayan Haq, Mattijs Jonker, Rol Van Rijswijk-Deij, Kimberly C Claffy, Lambert JM Nieuwenhuis, and Abhishta Abhishta

  36. [36]

    In2022 IEEE European Sympo- sium on Security and Privacy Workshops (EuroS&PW)

    No time for downtime: understanding post-attack behaviors by customers of managed DNS providers. In2022 IEEE European Sympo- sium on Security and Privacy Workshops (EuroS&PW). IEEE, 322–331

  37. [37]

    Nguyen Phong Hoang, Sadie Doreen, and Michalis Polychronakis

  38. [38]

    In9th USENIX Workshop on Free and Open Communications on the Internet (FOCI 19)

    Measuring {I2P} censorship at a global scale. In9th USENIX Workshop on Free and Open Communications on the Internet (FOCI 19)

  39. [39]

    Bradley Huffaker, Marina Fomenkov, and KC Claffy. 2011. Geocom- pare: a comparison of public and commercial geolocation databases. Proc. NMMC(2011), 1–12

    2011

  40. [40]

    Costas Iordanou, Georgios Smaragdakis, Ingmar Poese, and Nikolaos Laoutaris. 2018. Tracing cross border web tracking. InProceedings of 14 Not All Roads Lead to Rome the internet measurement conference 2018. 329–342

    2018

  41. [41]

    2025.IPinfo - The Trusted Source For IP Address Data

    IPinfo.io. 2025.IPinfo - The Trusted Source For IP Address Data. https: //ipinfo.io

    2025

  42. [42]

    Lin Jin, Shuai Hao, Haining Wang, and Chase Cotton. 2021. Under- standing the practices of global censorship through accurate, end- to-end measurements.Proceedings of the ACM on Measurement and Analysis of Computing Systems5, 3 (2021), 1–25

    2021

  43. [43]

    Jordan Jueckstock, Shaown Sarker, Peter Snyder, Panagiotis Pa- padopoulos, Matteo Varvello, Benjamin Livshits, and Alexandros Kapravelos. 2019. The blind men and the internet: Multi-vantage point web measurements.arXiv preprint arXiv:1905.08767(2019)

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  44. [44]

    Arash Molavi Kakhki, Samuel Jero, David Choffnes, Cristina Nita- Rotaru, and Alan Mislove. 2017. Taking a long look at QUIC: an ap- proach for rigorous evaluation of rapidly evolving transport protocols. Inproceedings of the 2017 internet measurement conference. 290–303

    2017

  45. [45]

    Aqsa Kashaf, Vyas Sekar, and Yuvraj Agarwal. 2020. Analyzing third party service dependencies in modern web services: Have we learned from the mirai-dyn incident?. InProceedings of the ACM Internet Mea- surement Conference. 634–647

    2020

  46. [46]

    Ethan Katz-Bassett, John P John, Arvind Krishnamurthy, David Wetherall, Thomas Anderson, and Yatin Chawathe. 2006. Towards IP geolocation using delay and topology measurements. InProceedings of the 6th ACM SIGCOMM conference on Internet measurement. 71–84

    2006

  47. [47]

    Etienne Khan, Anna Sperotto, Jeroen van der Ham, and Roland van Rijswijk-Deij. 2023. Stranger VPNs: Investigating the geo-unblocking capabilities of commercial VPN providers. InInternational Conference on Passive and Active Network Measurement. Springer, 46–68

    2023

  48. [48]

    Mohammad Taha Khan, Joe DeBlasio, Geoffrey M Voelker, Alex C Snoeren, Chris Kanich, and Narseo Vallina-Rodriguez. 2018. An em- pirical analysis of the commercial vpn ecosystem. InProceedings of the internet measurement conference 2018. 443–456

    2018

  49. [49]

    Bustamante

    Rashna Kumar, Sana Asif, Elise Lee, and Fabián E. Bustamante. 2023. Each at its Own Pace: Third-Party Dependency and Centralization Around the World. InProceedings of the ACM SIGMETRICS Interna- tional Conference on Measurement and Modeling of Computer Systems. ACM. doi:10.1145/3579437

    work page doi:10.1145/3579437 2023

  50. [50]

    Rashna Kumar, Esteban Carisimo, Lukas De Angelis Riva, Mauricio Buzzone, Fabi’an E Bustamante, Ihsan Ayyub Qazi, and Mariano G Beiró. 2024. Of Choices and Control-A Comparative Analysis of Government Hosting. InProceedings of the 2024 ACM on Internet Mea- surement Conference. 462–479

    2024

  51. [51]

    Hyunwoo Lee, Doowon Kim, and Yonghwi Kwon. 2021. TLS 1.3 in practice: How TLS 1.3 contributes to the internet. InProceedings of the Web Conference 2021. 70–79

    2021

  52. [52]

    Kevin Lee, Sten Sjöberg, and Arvind Narayanan. 2022. Password policies of most top websites fail to follow best practices. InEighteenth Symposium on Usable Privacy and Security (SOUPS 2022). 561–580

    2022

  53. [53]

    Timothy Libert. 2015. Exposing the hidden web: An analysis of third-party HTTP requests on 1 million websites.arXiv preprint arXiv:1511.00619(2015)

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  54. [54]

    Aniss Maghsoudlou, Lukas Vermeulen, Ingmar Poese, and Oliver Gasser. 2023. Characterizing the VPN Ecosystem in the Wild. In International Conference on Passive and Active Network Measurement. Springer, 18–45

    2023

  55. [55]

    Allison McDonald, Matthew Bernhard, Luke Valenta, Benjamin Van- derSloot, Will Scott, Nick Sullivan, J Alex Halderman, and Roya Ensafi

  56. [56]

    InProceedings of the Internet Measurement Conference 2018

    403 forbidden: a global view of CDN geoblocking. InProceedings of the Internet Measurement Conference 2018. 218–230

    2018

  57. [57]

    MeasurementLab. [n. d.]. Measurement Lab: Measure the Internet, save the data, and make it universally accessible and useful. https: //www.measurementlab.net/status/. Accessed 2025-05-05

    2025

  58. [58]

    Xianghang Mi, Xuan Feng, Xiaojing Liao, Baojun Liu, XiaoFeng Wang, Feng Qian, Zhou Li, Sumayah Alrwais, Limin Sun, and Ying Liu. 2019. Resident evil: Understanding residential ip proxy as a dark service. In 2019 IEEE symposium on security and privacy (SP). IEEE, 1185–1201

    2019

  59. [59]

    Ricky KP Mok, Hongyu Zou, Rui Yang, Tom Koch, Ethan Katz-Bassett, and Kimberly C Claffy. 2021. Measuring the network performance of google cloud platform. InProceedings of the 21st ACM internet measurement conference. 54–61

    2021

  60. [60]

    Arian Akhavan Niaki, Shinyoung Cho, Zachary Weinberg, Nguyen Phong Hoang, Abbas Razaghpanah, Nicolas Christin, and Phillipa Gill. 2020. ICLab: A global, longitudinal internet censorship measurement platform. In2020 IEEE symposium on security and privacy (SP). IEEE, 135–151

    2020

  61. [61]

    NLNOG. [n. d.]. NLNOG RING. https://ring.nlnog.net/nodes/. Accessed 2025-05-05

    2025

  62. [62]

    Daiyuu Nobori and Yasushi Shinjo. 2014. VPN Gate: A Volunteer Organized Public VPN Relay System with Blocking Resistance for Bypassing Government Censorship Firewalls. In11th USENIX Sym- posium on Networked Systems Design and Implementation (NSDI 14). 229–241

    2014

  63. [63]

    OpenAI. 2023. ChatGPT. https://openai.com/chatgpt. Accessed: 2025-07-29

    2023

  64. [64]

    Oxylabs. 2025. Oxylabs - High Quality Proxy Service to Gather Data at Scale. https://oxylabs.io. Accessed: 2025-07-29

    2025

  65. [65]

    Paul Pearce, Ben Jones, Frank Li, Roya Ensafi, Nick Feamster, Nick Weaver, and Vern Paxson. 2017. Global measurement of {DNS} ma- nipulation. In26th USENIX Security Symposium (USENIX Security 17). 307–323

    2017

  66. [66]

    PlanetLab. [n. d.]. PlanetLab EU Testbed. https://www.planet-lab.eu/. Accessed 2025-05-05

    2025

  67. [67]

    Reethika Ramesh, Leonid Evdokimov, Diwen Xue, and Roya Ensafi

  68. [68]

    InProceedings of the 29th Network and Distributed System Security Symposium (NDSS)

    VPNalyzer: Systematic Investigation of the VPN Ecosystem. InProceedings of the 29th Network and Distributed System Security Symposium (NDSS). Internet Society. doi:10.14722/ndss.2022.24285

    work page doi:10.14722/ndss.2022.24285 2022

  69. [69]

    Reethika Ramesh, Philipp Winter, Sam Korman, and Roya Ensafi. 2024. CalcuLatency: Leveraging Cross-Layer Network Latency Measure- ments to Detect Proxy-Enabled Abuse. InProceedings of the 33rd USENIX Security Symposium. USENIX Association. https://www. usenix.org/conference/usenixsecurity24/presentation/ramesh

    2024

  70. [70]

    Philipp Richter, Ramakrishna Padmanabhan, Neil Spring, Arthur Berger, and David Clark. 2018. Advancing the art of internet edge out- age detection. InProceedings of the Internet Measurement Conference

    2018

  71. [71]

    Sebastian Roth, Stefano Calzavara, Moritz Wilhelm, Alvise Rabitti, and Ben Stock. 2022. The Security Lottery: Measuring {Client-Side} Web Security Inconsistencies. In31st USENIX Security Symposium (USENIX Security 22). 2047–2064

    2022

  72. [72]

    Tomer Schwartz, Ofir Manor, and Andikan Otung. 2025. SNITCH: Leveraging IP Geolocation for Active VPN Detection. InWorkshop on Measurements, Attacks, and Defenses for the Web (MADWeb). NDSS Symposium. doi:10.14722/madweb.2025.23008

    work page doi:10.14722/madweb.2025.23008 2025

  73. [73]

    Semrush. 2025. Semrush: Data-Driven Marketing Tools to Grow Your Business. https://www.semrush.com. Accessed: 2025-07-21

    2025

  74. [74]

    Pavlos Sermpezis, Lars Prehn, Sofia Kostoglou, Marcel Flores, Athena Vakali, and Emile Aben. 2023. Bias in internet measurement plat- forms. In2023 7th Network Traffic Measurement and Analysis Confer- ence (TMA). IEEE, 1–10

    2023

  75. [75]

    Similarweb. 2025. Similarweb: AI-Powered Digital Data Intelligence Solutions. https://www.similarweb.com. Accessed: 2025-07-21

    2025

  76. [76]

    Rachee Singh, Arun Dunna, and Phillipa Gill. 2018. Characterizing the deployment and performance of multi-cdns. InProceedings of the Internet Measurement Conference 2018. 168–174. 15 Sachin Kumar Singh, Robert Ricci, and Alexander Gamero-Garrido

    2018

  77. [77]

    Sachin Kumar Singh, Robert Ricci, and Alexander Gamero-Garrido

  78. [78]

    InProceedings of the 2025 ACM Internet Measurement Conference

    Where in the World Are My Trackers? Mapping Web Tracking Flow Across Diverse Geographic Regions. InProceedings of the 2025 ACM Internet Measurement Conference. 692–708

    2025

  79. [79]

    2024.Page Weight

    Dave Smart and Jamie Indigo. 2024.Page Weight. HTTP Archive. https://almanac.httparchive.org/en/2024/page-weight Accessed: 2025-10-01

    2024

  80. [80]

    Raffaele Sommese, Leandro Bertholdo, Gautam Akiwate, Mattijs Jonker, Roland van Rijswijk-Deij, Alberto Dainotti, Kimberly C Claffy, and Anna Sperotto. 2020. Manycast2: Using anycast to measure any- cast. InProceedings of the ACM Internet Measurement Conference. 456– 463

    2020

Showing first 80 references.