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Multi-platform data collection for public service with Pay-by-Data

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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 multi-platform 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.

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Notes

  1. Appthority. App Reputation Report Winter 2014: https://www.appthority.com/app-reputation-report/report/AppReputation0214.pdf, December 2014

  2. Shara Tibken, Path to pay $800,000 to settle privacy issues with FTD. From CNet: http://www.cnet.com/uk/news/path-to-pay-800000-to-settle-privacy-issues-with-ftc/, February 2013

  3. Imperial Festival: https://www.imperial.ac.uk/be-inspired/festival/whats-on/activities/mobile-data-whats-yours-worth-2.php

  4. Digital Economy 2015: http://mitsloan.mit.edu/alumni/events/2015-london-digital-economy/

  5. Pay-by-data Android: https://github.com/Guanjie-Liu/Pay-By-Data-Android/wiki/Compiling-&-Installing-PBD-OS

  6. PBD SDK: https://github.com/Guanjie-Liu/Pay-By-Data-Android/wiki/Compiling-&-Importing-PBD-SDK

  7. React Native: http://facebook.github.io/react-native/

  8. Blackphone: https://www.silentcircle.com/products-and-solutions/devices/

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Acknowledgments

This work is supported by Fundamental Research Funds for the Central Universities, Artificial Intelligence Research Foundation of Baidu Inc., Zhejiang University and Cybervein Joint Research Lab, Zhejiang Natural Science Foundation (LY19F020051, R19F020009, LZ17F020001), National Natural Science Foundation of China (61572431), Key R&D Program of Zhejiang Province (2018C01006), Program of China Knowledge Center for Engineering Sciences and Technology, Program of ZJU and Tongdun Joint Research Lab, Program of ZJU and Horizon Robotics Joint Research Lab, Joint Research Program of ZJU and Hikvision Research Institute, and Major Scientifc Research Project of Zhejiang Lab (No. 2018EC0ZX01-1).

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Authors and Affiliations

  1. School of Public Affairs, Zhejiang University, MMW BLDG, 866 Yuhangtang Road, Hangzhou, 310058, China

    Chao Wu

  2. Data Science Institute, Imperial College London, London, UK

    Chao Wu

  3. School of Civil Engineering, Zhejiang University, Hangzhou, China

    Simon Hu

  4. Transport and Environmental Laboratory, Imperial College London, London, UK

    Simon Hu

  5. Department of Computing, Imperial College London, London, UK

    Chun-Hsiang Lee

  6. College of Computer Science, Zhejiang University, Hangzhou, 310000, China

    Jun Xiao

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  1. Chao Wu
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  2. Simon Hu
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  3. Chun-Hsiang Lee
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  4. Jun Xiao
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Correspondence to Jun Xiao.

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Wu, C., Hu, S., Lee, CH. et al. Multi-platform data collection for public service with Pay-by-Data. Multimed Tools Appl 79, 33503–33518 (2020). https://doi.org/10.1007/s11042-019-07919-0

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