Title :

Decentralised and Privacy-Aware Learning of Traversal Time Models

Author(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]

Conference title :

ECML PKDD 2017 - European Conference on Machile Learning & Principles and Practice of Knowledge Discovery in Databases : workshop DMSC - Data Mining with Secure Computation

City :

Skopje

Country :

Macédoine

Start date of the conference :

2017-09-18

Publication date :

2017

English keyword(s) :

Traversal time estimation
privacy
decentralised learning

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL