Decentralised and Privacy-Aware Learning ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Decentralised and Privacy-Aware Learning of Traversal Time Models
Auteur(s) :
Le Van, Thanh [Auteur]
Machine Learning in Information Networks [MAGNET]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Machine Learning in Information Networks [MAGNET]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Titre de la manifestation scientifique :
ECML PKDD 2017 - European Conference on Machile Learning & Principles and Practice of Knowledge Discovery in Databases : workshop DMSC - Data Mining with Secure Computation
Ville :
Skopje
Pays :
Macédoine
Date de début de la manifestation scientifique :
2017-09-18
Date de publication :
2017
Mot(s)-clé(s) en anglais :
Traversal time estimation
privacy
decentralised learning
privacy
decentralised learning
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
Estimating traversal time is an essential problem in urban computing. Traditional methods learn a predictive model from user traces collected in a central server, which potentially threatens the privacy of the users, and ...
Lire la suite >Estimating traversal time is an essential problem in urban computing. Traditional methods learn a predictive model from user traces collected in a central server, which potentially threatens the privacy of the users, and which may be hard to realize in an online setting where communication with large amounts of cars is needed. In this paper, we propose a new approach to solve these problems by proposing a a privacy-friendly algorithm requiring only local communication. First, we introduce a new optimisation-based formalisation, which can take into account user-specific driving styles and the homophily of the traffic in road networks. We then discuss how we can solve this problem in a decentralised setting, where each user stores his/her sensitive data locally (without uploading it to a central server) and only shares indirect information in a peer-to-peer manner. Finally, we discuss strategies to learn the model without revealing sensitive information such as locations and user identities.Lire moins >
Lire la suite >Estimating traversal time is an essential problem in urban computing. Traditional methods learn a predictive model from user traces collected in a central server, which potentially threatens the privacy of the users, and which may be hard to realize in an online setting where communication with large amounts of cars is needed. In this paper, we propose a new approach to solve these problems by proposing a a privacy-friendly algorithm requiring only local communication. First, we introduce a new optimisation-based formalisation, which can take into account user-specific driving styles and the homophily of the traffic in road networks. We then discuss how we can solve this problem in a decentralised setting, where each user stores his/her sensitive data locally (without uploading it to a central server) and only shares indirect information in a peer-to-peer manner. Finally, we discuss strategies to learn the model without revealing sensitive information such as locations and user identities.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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