arXiv subjects

Fabián E. Bustamante

Publications and source records attributed to Fabián E. Bustamante.

5 recordsLinked to original sources

arXiv2026

Overconfident Coordinates: Quantifying Confidence in Traceroute Geolocation

Studies of Internet paths often attach router locations to traceroute hops using commercial geolocation databases, rDNS labels, Geofeeds, and IXP metadata. These sources provide useful hints, but they report point locations without calibrated confidence, leaving researchers unable to tell whether a geographic path is trustworthy. We introduce Path Consistency Scoring (PCS), a passive framework that evaluates router geolocation as a path-level consistency problem. PCS models each traceroute as a sequence of candidate city-level locations and uses a Hidden Markov Model to fuse local evidence with speed-of-light constraints and empirical latency priors. PCS produces a path consistency score summarizing how well metadata and observed RTT increments support a coherent geographic interpretation. Because this score is only meaningful when latency proxies for geography, we also define a Path-Model Alignment metric that compares speed-of-light residual increments of the decoded path against a reference path. We evaluate on 413,354 RIPE Atlas traceroutes and a 6,555-path subset verified by active probing. On validated paths, 94.2% of decoded sequences achieve mean error below 200 km. PCS is largely GeoDB-agnostic; median scores vary by less than 5% across four commercial databases, while the alignment metric reveals that over half of DB-IP and IP2Location paths require substantial correction, compared with 15% for IPinfo. This lets downstream analyses quantify confidence in their geographic conclusions rather than inheriting database accuracy without qualification.

cs.NI↗

arXiv2026

Linnaeus: A Hierarchical, Multi-Label Framework for Autonomous System Classification

Autonomous systems (ASes) play diverse roles in today's Internet, from community and research backbones to hyperscale content providers and submarine-cable operators. However, existing taxonomies based solely on network-level features fail to capture their semantic and operational heterogeneity. In this paper, we present Linnaeus, a hierarchical AS-classification framework that combines network-centric data (e.g., topology, BGP announcements) with rich non-network features and leverages domain-adapted large language models alongside traditional machine-learning techniques. Linnaeus provides a two-level taxonomy with 18 top-level and 38 second-level classes, supports multi-label assignments to reflect hybrid roles (e.g., research backbone and transit provider), and provides an end-to-end pipeline from data ingestion to label inference. On a manually annotated dataset of nearly 2,000 ASes, Linnaeus achieves an overall precision and recall of 0.83 and 0.76, respectively. We further demonstrate its practical value through case studies, highlighting Linnaeus's potential to reveal both structural and semantic dimensions of Internet infrastructure.

cs.NI↗

arXiv2025

Measuring the Accuracy and Effectiveness of PII Removal Services

This paper presents the first large-scale empirical study of commercial personally identifiable information (PII) removal systems -- commercial services that claim to improve privacy by automating the removal of PII from data broker's databases. Popular examples of such services include DeleteMe, Mozilla Monitor, Incogni, among many others. The claims these services make may be very appealing to privacy-conscious Web users, but how effective these services actually are at improving privacy has not been investigated. This work aims to improve our understanding of commercial PII removal services in multiple ways. First, we conduct a user study where participants purchase subscriptions from four popular PII removal services, and report (i) what PII the service find, (ii) from which data brokers, (iii) whether the service is able to have the information removed, and (iv) whether the identified information actually is PII describing the participant. And second, by comparing the claims and promises the services makes (e.g. which and how many data brokers each service claims to cover). We find that these services have significant accuracy and coverage issues that limit the usefulness of these services as a privacy-enhancing technology. For example, we find that the measured services are unable to remove the majority of the identified PII records from data broker's (48.2% of the successfully removed found records) and that most records identified by these services are not PII about the user (study participants found that only 41.1% of records identified by these services were PII about themselves).

cs.CR↗

arXiv2023

A hop away from everywhere: A view of the intercontinental long-haul infrastructure

Over the past two decades, a desire to reduce transit cost, improve control over routing and performance, and enhance the quality of experience for users, has yielded a more densely connected, flat network with fewer hops between sources and destinations. The shortening of paths in terms of the number of hops or links has also meant, for what is at the end an infrastructure-bound network, the lengthening of many of these links. In this paper, we focus on an important aspect of the evolving logical connectivity of the Internet that has received little attention to date: intercontinental long-haul links. We develop a methodology and associated processing system for identifying long haul links in traceroute measurements. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. We identify over 9K layer 3 links that satisfy our definition for intercontinental long haul with many of them terminating in a relatively small number of nodes. An analysis of connected components shows a clearly dominant one with a relative size that remains stable despite a significant growth of the long-haul infrastructure.

cs.NI↗

arXiv2023

Reclaiming Privacy and Performance over Centralized DNS

The Domain Name System (DNS) is both a key determinant of users' quality of experience (QoE) and privy to their tastes, preferences, and even the devices they own. Growing concern about user privacy and QoE has brought a number of alternative DNS services, from public DNS to encrypted and Oblivious DNS. While offering valuable features, these DNS variants are operated by a handful of providers, reinforcing a trend towards centralization that has raised concerns about privacy, competition, resilience and Web QoE. The goal of this work is to let users take advantage of third-party DNS services, without sacrificing privacy or performance. We follow Wheeler's advice, adding another level of indirection with an end-system DNS resolver, Onoma, that improves privacy, avoiding DNS-based user-reidentification by inserting and sharding requests across resolvers, and improves performance by running resolution races among resolvers and reinstating the client-resolver proximity assumption content delivery networks rely on. As our evaluation shows, while there may not be an ideal service for all clients in all places, Onoma dynamically finds the best service for any given location.

cs.CR↗