Méthodes et utiles pour améliorer la transparence de la publicité en ligne

par Minh kha Nguyen

Projet de thèse en Informatique

Sous la direction de Sihem Amer-Yahia.

Thèses en préparation à Grenoble Alpes , dans le cadre de École doctorale mathématiques, sciences et technologies de l'information, informatique (Grenoble) , en partenariat avec Laboratoire d'Informatique de Grenoble (laboratoire) depuis le 23-11-2018 .


  • Résumé

    In recent years, targeted advertising has been source of a growing number of privacy complaints from Internet users [1]. At the heart of the problem lies the opacity of the targeted advertising mechanisms: users do not understand what data advertisers have about them and how this data is being used to select the ads they are being shown. This lack of transparency has begun to catch the attention of policy makers and government regulators, which are increasingly introducing laws requiring transparency [2]. The project consists in developing methods and tools that provide explanations for why a user has been targeted with a particular ad that does not need the collaboration of the advertising platform. The key idea is to crowdsource the transparency task to users. The project consists in designing methods and building a collaborative tool where users donate in a privacy-preserving manner data about the ads they receive. The platform needs to aggregate data from multiple users (using machine learning) to infer (statistically) why a user received a particular ad. Intuitively, we will group together all users that received the same ad, and look at the most common demographics and interests of users in the group. The key challenge is to identify the limits of what we can statistically infer from such a platform.

  • Titre traduit

    Methods and tools to increase the transparency of online advertising


  • Résumé

    In recent years, targeted advertising has been source of a growing number of privacy complaints from Internet users [1]. At the heart of the problem lies the opacity of the targeted advertising mechanisms: users do not understand what data advertisers have about them and how this data is being used to select the ads they are being shown. This lack of transparency has begun to catch the attention of policy makers and government regulators, which are increasingly introducing laws requiring transparency [2]. The project consists in developing methods and tools that provide explanations for why a user has been targeted with a particular ad that does not need the collaboration of the advertising platform. The key idea is to crowdsource the transparency task to users. The project consists in designing methods and building a collaborative tool where users donate in a privacy-preserving manner data about the ads they receive. The platform needs to aggregate data from multiple users (using machine learning) to infer (statistically) why a user received a particular ad. Intuitively, we will group together all users that received the same ad, and look at the most common demographics and interests of users in the group. The key challenge is to identify the limits of what we can statistically infer from such a platform.