arXiv subjects

Rubén Cuevas

Publications and source records attributed to Rubén Cuevas.

15 recordsLinked to original sources

arXiv2025

adF: A Novel System for Measuring Web Fingerprinting through Ads

This paper introduces adF, a novel system for analyzing the vulnerability of different devices, Operating Systems (OSes), and browsers to web fingerprinting. adF performs its measurements from code inserted in ads. We have used our system in several ad campaigns that delivered 5.40 million ad impressions. The collected data allow us to assess the vulnerability of current desktop and mobile devices to web fingerprinting. Based on our results, we estimate that 66% of desktop devices and 40% of mobile devices can be uniquely fingerprinted with our web fingerprinting system. However, the resilience to web fingerprinting varies significantly across browsers and device types, with Chrome on desktops being the most vulnerable configuration. To counter web fingerprinting, we propose ShieldF, a simple solution which blocks the reporting by browsers of those attributes that we found in the analysis of our dataset that present the most significant discrimination power. Our experiments reveal that ShieldF outperforms all anti-fingerprinting solutions proposed by major browsers (Chrome, Safari and Firefox) offering an increase in the resilience offered to web fingerprinting up to 62% for some device configurations. ShieldF is available as an add-on for any chromium-based browser. Moreover, it is readily adoptable by browser and mobile app developers. Its widespread use would lead to a significant improvement in the protection offered by browsers and mobile apps to web fingerprinting.

cs.CR↗

arXiv2025

From Prompts to Power: Measuring the Energy Footprint of LLM Inference

The rapid expansion of Large Language Models (LLMs) has introduced unprecedented energy demands, extending beyond training to large-scale inference workloads that often dominate total lifecycle consumption. Deploying these models requires energy-intensive GPU infrastructure, and in some cases has even prompted plans to power data centers with nuclear energy. Despite this growing relevance, systematic analyses of inference energy consumption remain limited. In this work, we present a large-scale measurement-based study comprising over 32,500 measurements across 21 GPU configurations and 155 model architectures, from small open-source models to frontier systems. Using the vLLM inference engine, we quantify energy usage at the prompt level and identify how architectural and operational factors shape energy demand. Building on these insights, we develop a predictive model that accurately estimates inference energy consumption across unseen architectures and hardware, and implement it as a browser extension to raise awareness of the environmental impact of generative AI.

cs.AI↗

arXiv2025

Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis

Time-series classification is essential across diverse domains, including medical diagnosis, industrial monitoring, financial forecasting, and human activity recognition. The Rocket algorithm has emerged as a simple yet powerful method, achieving state-of-the-art performance through random convolutional kernels applied to time-series data, followed by non-linear transformation. Its architecture approximates a one-hidden-layer convolutional neural network while eliminating parameter training, ensuring computational efficiency. Despite its empirical success, fundamental questions about its theoretical foundations remain unexplored. We bridge theory and practice by formalizing Rocket's random convolutional filters within the compressed sensing framework, proving that random projections preserve discriminative patterns in time-series data. This analysis reveals relationships between kernel parameters and input signal characteristics, enabling more principled approaches to algorithm configuration. Moreover, we demonstrate that its non-linearity, based on the proportion of positive values after convolutions, expresses the inherent sparsity of time-series data. Our theoretical investigation also proves that Rocket satisfies two critical conditions: translation invariance and noise robustness. These findings enhance interpretability and provide guidance for parameter optimization in extreme cases, advancing both theoretical understanding and practical application of time-series classification.

stat.ML↗

arXiv2024

Digital Advertising in a Post-Cookie World: Charting the Impact of Google's Topics API

Integrating Google's Topics API into the digital advertising ecosystem represents a significant shift toward privacy-conscious advertising practices. This article analyses the implications of implementing Topics API on ad networks, focusing on competition dynamics and ad space accessibility. Through simulations based on extensive datasets capturing user behavior and market share data for ad networks, we evaluate metrics such as Ad Placement Eligibility, Low Competition Rate, and solo competitor. The findings reveal a noticeable impact on ad networks, with larger players strengthening their dominance and smaller networks facing challenges securing ad spaces and competing effectively. Moreover, the study explores the potential environmental implications of Google's actions, highlighting the need to carefully consider policy and regulatory measures to ensure fair competition and privacy protection. Overall, this research contributes valuable insights into the evolving dynamics of digital advertising and highlights the importance of balancing privacy with competition and innovation in the online advertising landscape.

cs.SI↗

arXiv2024

Analysis and implementation of nanotargeting on LinkedIn based on publicly available non-PII

The literature has shown that combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. This work demonstrates that a combination of a few non-PII items can be activated to nanotarget users. We demonstrate that the combination of the location and {5} rare ({13} random) skills in a LinkedIn profile is enough to become unique in a user base of {$\sim$970M} users with a probability of 75\%. The novelty is that these attributes are publicly accessible to anyone registered on LinkedIn and can be activated through advertising campaigns. We ran an experiment configuring ad campaigns using the location and skills of three of the paper's authors, demonstrating how all the ads using $\geq13$ skills were delivered exclusively to the targeted user. We reported this vulnerability to LinkedIn, which initially ignored the problem, but fixed it as of November 2023.%This nanotargeting may expose LinkedIn users to privacy and security risks such as malvertising or manipulation.

cs.SI↗

arXiv2023

Time Series Clustering With Random Convolutional Kernels

Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.

cs.LG↗

arXiv2023

CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online Ads

Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors' knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.

cs.CY↗

arXiv2022

A deep dive into the accuracy of IP Geolocation Databases and its impact on online advertising

The quest for every time more personalized Internet experience relies on the enriched contextual information about each user. Online advertising also follows this approach. Among the context information that advertising stakeholders leverage, location information is certainly one of them. However, when this information is not directly available from the end users, advertising stakeholders infer it using geolocation databases, matching IP addresses to a position on earth. The accuracy of this approach has often been questioned in the past: however, the reality check on an advertising DSP shows that this technique accounts for a large fraction of the served advertisements. In this paper, we revisit the work in the field, that is mostly from almost one decade ago, through the lenses of big data. More specifically, we, i) benchmark two commercial Internet geolocation databases, evaluate the quality of their information using a ground truth database of user positions containing more than 2 billion samples, ii) analyze the internals of these databases, devising a theoretical upper bound for the quality of the Internet geolocation approach, and iii) we run an empirical study that unveils the monetary impact of this technology by considering the costs associated with a real-world ad impressions dataset. We show that when factoring cost in, IP geolocation technology may be, under certain campaign characteristics, a better alternative than GPS from an economic point of view, despite its inferior performance.

cs.CY↗

arXiv2022

How resilient is the Open Web to the COVID-19 pandemic?

In this paper we refer to the Open Web to the set of services offered freely to Internet users, representing a pillar of modern societies. Despite its importance for society, it is unknown how the COVID-19 pandemic is affecting the Open Web. In this paper, we address this issue, focusing our analysis on Spain, one of the countries which have been most impacted by the pandemic. On the one hand, we study the impact of the pandemic in the financial backbone of the Open Web, the online advertising business. To this end, we leverage concepts from Supply-Demand economic theory to perform a careful analysis of the elasticity in the supply of ad-spaces to the financial shortage of the online advertising business and its subsequent reduction in ad spaces' price. On the other hand, we analyze the distribution of the Open Web composition across business categories and its evolution during the COVID-19 pandemic. These analyses are conducted between Jan 1st and Dec 31st, 2020, using a reference dataset comprising information from more than 18 billion ad spaces. Our results indicate that the Open Web has experienced a moderate shift in its composition across business categories. However, this change is not produced by the financial shortage of the online advertising business, because as our analysis shows, the Open Web's supply of ad spaces is inelastic (i.e., insensitive) to the sustained low-price of ad spaces during the pandemic. Instead, existing evidence suggests that the reported shift in the Open Web composition is likely due to the change in the users' online behavior (e.g., browsing and mobile apps utilization patterns).

cs.SI↗

arXiv2022

Digital Contact Tracing: Large-scale Geolocation Data as an Alternative to Bluetooth-based Apps' Failure

The currently deployed contact-tracing mobile apps have failed as an efficient solution in the context of the COVID-19 pandemic. None of them has managed to attract the number of active users required to achieve an efficient operation. This urges the research community to re-open the debate and explore new avenues that lead to efficient contact-tracing solutions. This paper contributes to this debate with an alternative contact-tracing solution that leverages already available geolocation information owned by BigTech companies with very large penetration rates in most countries adopting contact-tracing mobile apps. Moreover, our solution provides sufficient privacy guarantees to protect the identity of infected users as well as precluding Health Authorities from obtaining the contact graph from individuals.

cs.SI↗

arXiv2021

Unique on Facebook: Formulation and Evidence of (Nano)targeting Individual Users with non-PII Data

The privacy of an individual is bounded by the ability of a third party to reveal their identity. Certain data items such as a passport ID or a mobile phone number may be used to uniquely identify a person. These are referred to as Personal Identifiable Information (PII) items. Previous literature has also reported that, in datasets including millions of users, a combination of several non-PII items (which alone are not enough to identify an individual) can uniquely identify an individual within the dataset. In this paper, we define a data-driven model to quantify the number of interests from a user that make them unique on Facebook. To the best of our knowledge, this represents the first study of individuals' uniqueness at the world population scale. Besides, users' interests are actionable non-PII items that can be used to define ad campaigns and deliver tailored ads to Facebook users. We run an experiment through 21 Facebook ad campaigns that target three of the authors of this paper to prove that, if an advertiser knows enough interests from a user, the Facebook Advertising Platform can be systematically exploited to deliver ads exclusively to a specific user. We refer to this practice as nanotargeting. Finally, we discuss the harmful risks associated with nanotargeting such as psychological persuasion, user manipulation, or blackmailing, and provide easily implementable countermeasures to preclude attacks based on nanotargeting campaigns on Facebook.

cs.SI↗

arXiv2021

Establishing Trust in Online Advertising with Signed Transactions

Programmatic advertising operates one of the most sophisticated and efficient service platforms on the Internet. However, the complexity of this ecosystem is a direct cause of one of the most important problems in online advertising, the lack of transparency. This lack of transparency enables subsequent problems such as advertising fraud, which causes billions of dollars in losses. In this paper we propose Ads.chain, a technological solution to the lack-of-transparency problem in programmatic advertising. Ads.chain extends the current effort of the Internet Advertising Bureau (IAB) in providing traceability in online advertising through the Ads.txt and Ads.cert solutions, addressing the limitations of these techniques. Ads.chain is (to the best of the authors' knowledge) the first solution that provides end-to-end cryptographic traceability at the ad transaction level. It is a communication protocol that can be seamlessly embedded into ad-tags and the OpenRTB protocol, the de-facto standards for communications in online advertising, allowing an incremental adoption by the industry. We have implemented Ads.chain and made the code publicly available. We assess the performance of Ads.chain through a thorough analysis in a lab environment that emulates a real ad delivery process at real-life throughputs. The obtained results show that Ads.chain can be implemented with limited impact on the hardware resources and marginal delay increments at the publishers lower than 0.20 milliseconds per ad space on webpages and 2.6 milliseconds at the programmatic advertising platforms. These results confirm that Ads.chain's impact on the user experience and the overall operation of the programmatic ad delivery process can be considered negligible.

cs.CR↗

arXiv2019

Does Facebook Use Sensitive Data for Advertising Purposes? Worldwide Analysis and GDPR Impact

The recent European General Data Protection Regulation (GDPR) and other data protection regulations restrict the processing of some categories of personal data (health, political orientation, sexual preferences, religious beliefs, ethnic origin, etc.) due to the privacy risks associated to such information. The GDPR refers to these categories as sensitive personal data. This paper quantifies the portion of Facebook (FB) users, across 197 countries, who are labeled with advertising interests linked to potentially sensitive personal data. Our study reveals that Facebook labels 67% of users with potential sensitive interests. This corresponds to 22% of the population in the referred 197 countries. Moreover, our work shows that the GDPR enforcement had a negligible impact in this context since the portion of FB users labeled with sensitive interests in the European Union remains almost the same 5 months before and 9 months after the GDPR was enacted. The paper also illustrates potential risks associated to the use of sensitive interests. For instance, we quantify the portion of FB users labelled with the interest "Homosexuality" in countries where being gay may be punished with the death penalty. The last contribution is the implementation of a web browser extension that allows FB users removing in a simple way the potentially sensitive interests FB has assigned them.

cs.SI↗

arXiv2018

Large-scale analysis of user exposure to online advertising in Facebook

Online advertising is the major source of income for a large portion of Internet Services. There exists a body of literature aiming at optimizing ads engagement, understanding the privacy and ethical implications of online advertising, etc. However, to the best of our knowledge, no previous work analyses at large scale the exposure of real users to online advertising. This paper performs a comprehensive analysis of the exposure of users to ads and advertisers using a dataset including more than 7M ads from 140K unique advertisers delivered to more than 5K users that was collected between October 2016 and May 2018. The study focuses on Facebook, which is the second largest advertising platform only to Google in terms of revenue, and accounts for more than 2.2B monthly active users. Our analysis reveals that Facebook users are exposed (in median) to 70 ads per week, which come from 12 advertisers. Ads represent between 10% and 15% of all the information received in users' newsfeed. A small increment of 1% in the portion of ads in the newsfeed could roughly represent a revenue increase of 8.17M USD per week for Facebook. Finally, we also reveal that Facebook users are overprofiled since in the best case only 22.76% of the interests Facebook assigns to users for advertising purpose are actually related to the ads those users receive.

cs.SI↗

arXiv2018

Facebook Use of Sensitive Data for Advertising in Europe

The upcoming European General Data Protection Regulation (GDPR) prohibits the processing and exploitation of some categories of personal data (health, political orientation, sexual preferences, religious beliefs, ethnic origin, etc.) due to the obvious privacy risks that may be derived from a malicious use of such type of information. These categories are referred to as sensitive personal data. Facebook has been recently fined EUR 1.2M in Spain for collecting, storing and processing sensitive personal data for advertising purposes. This paper quantifies the portion of Facebook users in the European Union (EU) who are labeled with interests linked to sensitive personal data. The results of our study reveal that Facebook labels 73% EU users with sensitive interests. This corresponds to 40% of the overall EU population. We also estimate that a malicious third-party could unveil the identity of Facebook users that have been assigned a sensitive interest at a cost as low as EUR 0.015 per user. Finally, we propose and implement a web browser extension to inform Facebook users of the sensitive interests Facebook has assigned them.

cs.SI↗