Publications associées à Pistage web

Multi-Frequency Tracking via Group-Sparse Optimal Transport

In this letter, we introduce an optimal transport framework for inferring power distributions over both spatial location and temporal frequency. Recently, it has been shown that optimal transport is a powerful tool for estimating spatial spectra that chang ...

Ieee-Inst Electrical Electronics Engineers Inc2024

CookieGraph: Understanding and Detecting First-Party Tracking Cookies

Carmela González Troncoso, Sandra Deepthy Siby

As third-party cookie blocking is becoming the norm in mainstream web browsers, advertisers and trackers have started to use first-party cookies for tracking. To understand this phenomenon, we conduct a differential measurement study with versus without th ...

Assoc Computing Machinery2023

Protecting privacy through metadata analysis

Sandra Deepthy Siby

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...

EPFL2022

WEBGRAPH: Capturing Advertising and Tracking Information Flows for Robust Blocking

Carmela González Troncoso, Sandra Deepthy Siby

Users rely on ad and tracker blocking tools to protect their privacy. Unfortunately, existing ad and tracker blocking tools are susceptible to mutable advertising and tracking content. In this paper, we first demonstrate that a state-of-the-art ad and trac ...

USENIX ASSOC2022

A Framework for Adaptive User Interface Generation based on User Behavioural Patterns

Kalpani Anuradha Welivita

The concept of adaptivity is crucial in enterprise software systems with a large user base. Adaptive user interfaces (AUI) is an emerging research area that enables customized user experience based on user activities. Most of the existing studies that are ...

IEEE2019

Privacy in Recommender Systems

Mahsa Taziki

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

EPFL2018

Capturing the Moment: Lightweight Similarity Computations

Rachid Guerraoui, Rhicheek Patra, Georgios Damaskinos

Similarity computations are crucial in various web activities like advertisements, search or trust-distrust predictions. These similarities often vary with time as product perception and popularity constantly change with users' evolving inclination. The hu ...

IEEE2017

Enhancing Social Media Platforms for Educational and Humanitarian Knowledge Sharing : Analytics, Privacy, Discovery, and Delivery Aspects

Andrii Vozniuk

Social media (SM) platforms have demonstrated their ability to facilitate knowledge sharing on the global scale. They are increasingly often employed in educational and humanitarian domains where, despite their general benefits, they expose challenges pecu ...

EPFL2017

Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery

Denis Gillet, Maria Jesus Rodriguez Triana, Adrian Christian Holzer, Andrii Vozniuk

Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environ ...

2016

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Matthias Grossglauser, Lucas Maystre, Young Jun Ko

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...

2016