Multi-platform data collection for public service with Pay-by-Data
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Multi-platform data collection for public service with Pay-by-Data Chao Wu1,2 · Simon Hu3,4 · Chun-Hsiang Lee5 · Jun Xiao6 Received: 31 May 2018 / Revised: 19 May 2019 / Accepted: 21 June 2019 / © Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract In order to establish efficient public services (e.g., traffic management, demand forecasting, traffic prediction), it’s necessary to build a supportive data collection, specially multiplatform user data collection (e.g., data of user’s journey information), to provide training data for building models. However, several issues hinders such paradigm to be deployed in real world. Firstly, we need to achieve the balance between data collection and user privacy protection. Secondly, it’s crucial to motive the users to contribute their data. Thirdly, we need to design a data pricing mechanism to promote data sharing. In this paper, we try to solve these issues by extending the Pay-by-data model, which is an explicit data-service exchange protocol. Based on this, we propose a system framework to support large-scale public service. Keywords Multi-platform data collection · User data privacy · Pay by data · Public data service
1 Introduction Collection of sensitive user data has now become pervasive with the widespread use of mobile phones and wearable devices across multi-platforms, facilitated by numerous sensors (GPS, accelerometers, proximity and light sensors, cameras, gyroscopes, fingerprint Jun Xiao
[email protected] Chao Wu [email protected] 1
School of Public Affairs, Zhejiang University, MMW BLDG, 866 Yuhangtang Road, Hangzhou, 310058, China
2
Data Science Institute, Imperial College London, London, UK
3
School of Civil Engineering, Zhejiang University, Hangzhou, China
4
Transport and Environmental Laboratory, Imperial College London, London, UK
5
Department of Computing, Imperial College London, London, UK
6
College of Computer Science, Zhejiang University, Hangzhou, 310000, China
Multimedia Tools and Applications
sensors, heart rate monitors, etc.). Performing analytics on these user generated data at mass scale is clearly beneficial for public services [18], for instance the early detection of disease [19]. However, the current paradigm of user data collection also causes several concerns: 1. Privacy issue: Data privacy has been the central problem of the Internet for a long time [14]. 91% of iOS Apps and 83% of Android Apps carried out at least one risky behaviour.1 We have seen many companies (Facebook, Apple, Twitter, Yelp, etc.) being the focus of lawsuits, accused of distributing privacy-invading mobile Apps. A particular case was Path, a mobile social network that was caught uploading the address book of users including those under the age of 13.2 Technology blog VentureBeat reported that there were numerous other Apps that also uploaded user contact lists in similar ways to Path [16]. We found the loose regulation whereby detailed information on data acquisition is not indicated prior to installing Apps causes such
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