https://hdl.handle.net/20.500.12761/2 2026-09-15T08:06:37Z https://hdl.handle.net/20.500.12761/2070 Digital Consumption and Political Alignment: Evidence from Mobile Data Across Two European Elections in France Martínez-Durive, Orlando E.; Ucar, Iñaki; Smoreda, Zbigniew; Moro, Esteban; Fiore, Marco Understanding how digital platform consumption relates to political alignment is paramount in contemporary democracies, yet large-scale observational evidence spanning multiple platforms and elections remains scarce. We analyze passive mobile network metadata from a major French mobile network operator (MNO) covering approximately 31% of the mobile market across urban and suburban areas of metropolitan France during the 2019 and 2024 European Parliament elections. Integrating demands for tens of mobile services, including social media, news, messaging, and streaming, with socioeconomic indicators, we model vote shares via Dirichlet regression. We find that digital consumption provides independent and complementary signals of political alignment that, for several parties, match or exceed the predictive power of traditional socioeconomic indicators; combining both increases explanatory ability by up to 28.23%. For instance, right-wing support is associated with higher consumption of Facebook and TikTok, whereas engagement with online news outlets, Twitter, and Instagram correlates with centrist and progressive vote. These associations are consistent across 2019 and 2024, providing a population-scale view of the relationship between mobile platform use and political alignment. 2026-10-12T00:00:00Z https://hdl.handle.net/20.500.12761/2069 The Circuit Breaker That Cried Wolf: Measuring BGP Maximum-Prefix Exceedance and Connectivity Impact Martínez-Durive, Orlando E.; Chariton, Antonis; Fiore, Marco The BGP maximum-prefix limit serves as a network circuit breaker, tearing down sessions when announcements exceed a configured threshold. Despite its operational significance, little is known empirically about how accurately these limits reflect reality, how often they are exceeded, or what connectivity impact results from their exceedance. We present a first large-scale measurement study of BGP maximum-prefix limits in the wild, combining 12 months of RIPE RIS snapshots with PeeringDB metadata and CAIDA AS Rank data. Only 32.49% of RIS-visible ASes appear in PeeringDB, and 21--27% of recent limit fields carry a value of zero, indicating no declared limit; after filtering for recency and non-zero limits, 14,973 ASes form our analytical dataset. Among these, 14.55% (IPv4) and 11.24% (IPv6) exceed their declared limit intermittently, and 2.15% and 2.32% do so in every observed snapshot. These exceedances are driven by organic growth, such as new space acquisition or deaggregation, yet the mechanism responds identically to a route leak: Tier-1 and Major peers account for 18.4% of IPv4 lost sessions despite representing roughly 0.1% of all ASes. Based on our findings, we offer recommendations for operators and IXPs and call for renewed IETF standardization of dynamic limit negotiation. 2026-10-01T00:00:00Z https://hdl.handle.net/20.500.12761/2068 From 5G Single-Cell Signals to Pervasive Smartphone Positioning: A Real-World Study Eleftherakis, Stavros; Giustiniano, Domenico; Zhao, Yuxin; Jiang, Xiaolin; Lindmark, Gustav; Gunnarsson, Fredrik Accurate and reliable positioning is a cornerstone of pervasive computing. However, GNSS is not always available for pervasive services while cellular-network-based alternatives have proven unsuccessful, due to coarse position accuracy or complex network setup. In this work, we investigate 5G singlecell positioning by leveraging Timing Advance and Angle of Arrival measurements, two key indicators already used in 5G NR for communication. Our approach does not require tight network synchronization among base stations, unlike other 5G positioning techniques. We present the first real-world study of single-cell positioning in a 5G network with a commercial gNB and an off-the-shelf smartphone moving up to 461 meters away from the gNB across four urban trajectories. Our analysis reveals two key challenges: coarse angular and timing resolution, and severe angle and range errors under Non-Line-of-Sight (NLOS) conditions. To overcome these challenges, we present a framework that (i) synthesizes higher-resolution Angle-ofArrival estimates from real 5G beam patterns, (ii) classifies LOS/NLOS conditions with a Convolutional Neural Network trained on beam signal-strength heatmaps, and (iii) refines angle and ranging using multipath-aware corrections. Our system reduces the median positioning error from 86.8 m to 16.5 m with a single gNB, without the aid of external sensors. 2026-07-01T00:00:00Z https://hdl.handle.net/20.500.12761/2067 Rent-a-RAG: Embedding-Space Watermarks for Auditing Third-Party RAG Goultiaev Tolstokorov, Alexandr; Mouratidis, Kyriakos; Dogani, Javad; Laoutaris, Nikolaos Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet later have no visibility into whether their documents are being reused without compensation. Auditing this misuse is difficult because the operator is non-cooperative, answers are paraphrased by the generator, and one response may combine evidence from many providers. We propose DirBucket, a provider-side semantic watermarking and black-box auditing framework for document-level reuse in multi-provider RAG. DirBucket watermarks documents by meaning-preserving paraphrases whose embeddings are biased toward provider-bucket secret directions, enabling detection from black-box answers while preserving retrieval utility. On a challenging benchmark that reflects mixed-provider reuse under black-box access, DirBucket is the only method that consistently achieves strong target detection with no non-target activation, detecting non-compliance in every audit within 23 audited answers on our primary benchmark. The watermark survives adversarial post-answer laundering, and none of the evaluated evasion strategies simultaneously defeats detection while preserving user-perceived answer quality. Detection transfers unchanged to a second benchmark built from real clinical, cyber-threat-intelligence, and legal provider corpora. These results suggest that embedding-space watermarking can make document reuse in third-party RAG statistically auditable. 2026-10-01T00:00:00Z