Abstract
IP geolocation is a popular mechanism for determining the physical locations of Internet-connected devices. However, despite its widespread use, IP geolocation is known to be inaccurate, especially for devices in less industrialized nations. In 2020, geofeeds were standardized by the IETF, providing a mechanism for owners of IP addresses (i.e., autonomous systems) to self-report the physical locations of IP blocks under their control. Assuming IP address owners accurately report these locations, geofeeds conceptually have the potential to enable “groundtruth” location data. This short paper takes a first look at the roll-out of geofeeds. We examine the opt-in rates of geofeeds by autonomous systems, and surmise the use of geofeed data by two major IP geolocation providers. Over the course of our 14-month data collection efforts (August 2022–October 2023), the number of IP addresses covered by geofeeds has increased tenfold; however, the adoption rate is still low—less than 1% of the IPv4 address space is covered by geofeeds. We find that the rollout is also uneven, with more industrialized nations opting into geofeeds at rates higher than those of less industrialized ones. Moreover, our comparison of geofeed data to locations reported by commercial IP geolocation services suggests that these commercial services may be beginning to incorporate geofeed data into their resolutions. We discuss the implications of our findings, including the potential that uneven adoption rates may further disenfranchise Internet users in less industrialized nations.
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Notes
- 1.
For ease of exposition, we will often use the shorthand geolocation to refer to IP-based geolocation.
- 2.
- 3.
For example, while most commercial providers showed over 90% accuracy in identifying routers in the U.S., most providers showed between 20% and 39% accuracy when locating routers in Canada [28].
- 4.
Measurements were initially pulled manually once every two weeks and were later automated to run on the 13th and 28th of each month.
- 5.
To account for locales having numerous names or versions of the same name (e.g., the city name for Đakovo, Croatia could also be spelled Djakovo or Dakovo), we computed the normalized Damerau-Levenshtein distance [15] between the two location names and asserted that to match, the result had to be less than 0.5.
- 6.
To account for locations having multiple names or spellings, we used fuzzy matching with tokenized Levenshtein distance to find many of the named locations.
- 7.
References
Geopy documentation. https://geopy.readthedocs.io/en/latest/
IP geolocation database: Fastly Support. https://support.fastly.com/hc/en-us/community/posts/360078589732-IP-geolocation-database
CDN Comparison - Most Popular CDNs of 2021 (2021). https://www.experte.com/website/cdn
Seclists.org - re: Google IP geolocation (2021). https://seclists.org/nanog/2021/Apr/67
Digital Element (2022). https://www.digitalelement.com
Geoguard (2022). https://www.geocomply.com/products/geoguard/
Central Intelligence Agency: CIA world factbook. https://www.cia.gov/the-world-factbook/field/internet-users/country-comparison/
McDonald, A., et al.: 403 forbidden: a global view of CDN geoblocking. In: Proceedings of the Internet Measurement Conference 2018. IMC 2018. Association for Computing Machinery (2018)
Bano, S., et al.: Scanning the internet for liveness. SIGCOM Comput. Commun. Rev. 48(2), 2–9 (2018)
Wong, B., Stoyanov, I., Sirer, E.G.: Octant: a comprejensive framework for the geolocalization of internet hosts. In: 4th USENIX Symposium on Networked Systems Design & Implementation, NSDI 2007. USENIX Association (2007)
Cloudflare: Configuring Cloudflare IP Geolocation (2021). https://support.cloudflare.com/hc/en-us/articles/200168236-What-does-Cloudflare-IP-Geolocation-do-. Accessed 6 Dec 2021
Iordanou, C., Smaragdakis, G., Poese, I., Laoutaris, N.: Tracing cross border web tracking. In: Proceedings of the Internet Measurement Conference 2018, IMC 2018. Association for Computing Machinery (2018)
Dainotti, A., et al.: Estimating internet address space usage through passive measurements. SIGCOMM Comput. Commun. Rev. 44(1), 42–49 (2014)
Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964). https://dl.acm.org/doi/10.1145/363958.363994
Dingledine, R., Mathewson, N., Syverson, P.: Tor: the second-generation onion router. In: USENIX Security Symposium (USENIX) (2004)
Fainchtein, R.A., Aviv, A.J., Sherr, M.: User perceptions of the privacy and usability of smart DNS. In: Proceedings of the 38th Annual Computer Security Applications Conference, ACSAC 2022, pp. 591–604. Association for Computing Machinery (2022). https://dl.acm.org/doi/10.1145/3564625.3567978
Fainchtein, R.A., Aviv, A.J., Sherr, M., Ribaudo, S., Khullar, A.: Holes in the geofence: privacy vulnerabilities in “smart” DNS services. In: Proceedings on Privacy Enhancing Technologies (PoPETS) (2021)
Gueye, B., Ziviani, A., Crovella, M., Fdida, S.: Constraint based geolocation of internet hosts. In: Proceedings of the 2004 Internet Measurement Conference, IMC 2004. Association for Computing Machinery (2004)
Internet Corporation for Assigned Names and Numbers: About WHOIS (2022). https://whois.icann.org/en/about-whois
IPGeolocation: ip-geolocation-api-jquery-sdk (2022). https://www.jsdelivr.com/package/npm/ip-geolocation-api-jquery-sdk
ipgeolocation.io: IP geolocation API and IP lookup documentation. https://ipgeolocation.io/documentation.html
ipgeolocation.io: IP Geolocation FAQs. https://ipgeolocation.io/faq.html
Karney, C.F.: Algorithms for geodesics. J. Geodesy 87, 43–55 (2013)
Kline, E., Duleba, K., Szamonek, Z., Moser, S., Kumari, W.: A format for self-published IP geolocation feeds. Informational 8805, RFC-Editor (2020)
Kumar, R., Virkud, A., Raman, R.S., Prakash, A., Ensafi, R.: A large-scale investigation into geodifferences in mobile apps. In: 31st USENIX Security Symposium (USENIX Security 2022). USENIX Association (2022). https://www.usenix.org/conference/usenixsecurity22/presentation/kumar
Laki, S., Mátray, P., Hága, P., Sebők, T., Csabai, I., Vattay, G.: Spotter: a model based active geolocation service. In: 2011 Proceedings IEEE INFOCOM (2011)
Gharaibeh, M., Shah, A., Huffaker, B., Zhang, H., Ensafi, R., Papadopoulos, C.: A look at router geolocation in public and commercial databases. In: Proceedings of the Internet Measurement Conference 2017, IMC 2017. Association for Computing Machinery (2017)
Massimo Candela: geofeed-finder (2022). https://github.com/massimocandela/geofeed-finder
Maxmind: Geoip2 and geolite city and country databases. https://dev.maxmind.com/geoip/docs/databases/city-and-country
Maxmind: Geolocation accuracy. https://support.maxmind.com/hc/en-us/articles/4407630607131-Geolocation-Accuracy
Ziv, M., Izhikevich, L., Ruth, K., Izhikevich, K., Durumeric, Z.: ASdb: a system for classifying owners of autonomous systems. In: ACM Internet Measurement Conference (IMC) (2021)
Muir, J.A., Oorschot, P.C.V.: Internet geolocation: evasion and counterevasion. ACM Comput. Surv. 42(1), 1–23 (2009)
Namara, M., Wilkinson, D., Caine, K., Knijnenburg, B.P.: Emotional and practical considerations towards the adoption and abandonment of VPNs as a privacy-enhancing technology. Proc. Priv. Enh. Technol. (PoPETS) 2020(1), 83–102 (2020)
NRO: Delegated-extended-file. https://ftp.ripe.net/pub/stats/ripencc/nro-stats/latest/nro-delegated-stats. Accessed 15 Oct 2023
Richter, P., Smaragdakis, G., Plonka, D., Berger, A.: Beyond counting: new perspectives on the active IPv4 address space. In: Proceedings of the 2016 Internet Measurement Conference, IMC 2016. Association for Computing Machinery (2016)
Poese, I., Uhlig, S., Kaafar, M.A., Donnet, B., Gueye, B.: IP geolocation databases: unreliable? ACM SIGCOMM Comput. Commun. Rev. 41(2), 53–56 (2011)
Kumari, W., Bush, R., Candela, M., Housley, R.: RFC 9092 Finding and Using Geofeed Data. RFC Proposed Standard 9092, Internet Engineering Task Force (IETF) (2021)
Trimble, M.: The future of cybertravel: legal implications of the evasion of geolocation. Fordham Intell. Prop. Media Entertain. Law J. 22, 567 (2012)
Tschantz, M.C., Afroz, S., Sajid, S., Qazi, S.A., Javed, M., Paxson, V.: A bestiary of blocking: the motivations and modes behind website unavailability. In: 8th USENIX Workshop on Free and Open Communications on the Internet (FOCI 2018). USENIX Association (2018). https://www.usenix.org/conference/foci18/presentation/tschantz
Weinberg, Z., Cho, S., Christin, N., Sekar, V., Gill, P.: How to catch when proxies lie: verifying the physical locations of network proxies with active geolocation. In: ACM SIGCOMM Conference on Internet Measurement (IMC) (2018)
Acknowledgments
We thank the anonymous reviewers and shepherd for their invaluable feedback and suggestions. This work is partially funded by the National Science Foundation through grants 1925497 and 2138078, and by the Callahan Family Chair fund. The opinions and findings expressed in this paper are those of the authors and do not necessarily those of any employer or funding agency.
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Appendices
A Geofeed and Commercial IP Fetch Dates
Table 3 lists the dates of fetches for the geofeeds and the corresponding dates of the commercial IP datasets that were used for comparison.
B Country’s Representation Within the Geofeed Results
Table 4 presents a breakdown of the top ten most and bottom 20 least represented countries within the geofeed results before normalization.
Figure 9 provides a breakdown of countries’ representation within the geofeeds normalized by their respective number of Internet users [8] and Fig. 10 shows geofeeds normalized by each country’s IPv4 address allocation. Additionally, Fig. 11 provides a country-wise breakdown of the total ASes categorized as ISPs by the ASdb in the November 10, 2023 geofeed results and Fig. 12 denotes the proportion of ISPs amongst each represented country’s ASes in the same geofeed data.
Countries’ IPv4 address representation within the geofeeds normalized by number of Internet users [8].
Countries’ IPv4 address representation within the geofeeds normalized by their IPv4 address allocations.
Total number of ISPs in each country within the Geofeed Results for November 10, 2023 as categorized by the Stanford ASdb.
Proportion of each country’s ASNs that were categorized as ISPs by the Stanford ASdb in the November 10, 2023 Geofeed Results.
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Fainchtein, R.A., Sherr, M. (2024). You Can Find Me Here: A Study of the Early Adoption of Geofeeds. In: Richter, P., Bajpai, V., Carisimo, E. (eds) Passive and Active Measurement. PAM 2024. Lecture Notes in Computer Science, vol 14538. Springer, Cham. https://doi.org/10.1007/978-3-031-56252-5_11
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