Journal of Grid Computing

, Volume 14, Issue 4, pp 603–618 | Cite as

Privacy Preserving Geo-Linkage in the Big Urban Data Era

  • Richard O. Sinnott
  • Christopher Bayliss
  • Andrew Bromage
  • Gerson Galang
  • Yikai Gong
  • Philip Greenwood
  • Glenn Jayaputera
  • Davis Marques
  • Luca Morandini
  • Ghazal Nogoorani
  • Hossein Pursultani
  • Muhammad Sarwar
  • William Voorsluys
  • Ivo Widjaja
Article

Abstract

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.

Keywords

Big data Data privacy Geo-spatial systems Urban research 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Stimson, R., et al.: The Australian urban research infrastructure network (AURIN) initiative. State of Australian Cities, Melbourne (2011)Google Scholar
  3. 3.
    Sinnott, R. O., et al.: A data-driven urban research environment for Australia. IEEE e-Science Conference, Chicago (2012)CrossRefGoogle Scholar
  4. 4.
    Sinnott, R. O., et al.: The Australian urban research gateway. J. Concurr. Computat. Pract. Experience (2014). doi:10.1002/cpe.3282 Google Scholar
  5. 5.
    Sinnott, R. O., et al.: The urban data re-use and integration platform for australia: design, realisation and case studies. IEEE International Conference on Information Re-use and Integration, San Francisco (2015)Google Scholar
  6. 6.
    Sinnott, R. O., Voorsluys, W.: A scalable cloud-based system for data-intensive spatial analysis. Int. J. Tools Technol. Transfer, Springer (2015)Google Scholar
  7. 7.
    Randall, S. M., et al.: Privacy-preserving record linkage on large real world datasets. J. Biomed. Inform. 50, 205–212 (2014)CrossRefGoogle Scholar
  8. 8.
    Ritchie, F.: Secure access to confidential microdata: four years of the Virtual Microdata Laboratory. Econ Labour Mark. Rev. 2(5), 29–34 (2008)CrossRefGoogle Scholar
  9. 9.
    Smith, M., et al.: Big data privacy issues in public social media. In: 6th IEEE international conference on digital ecosystems and technologies (2012)Google Scholar
  10. 10.
    Mendoza, M., et al.: Twitter under crisis: can we trust what we RT?. In: Proceedings of the first workshop on social media analytics, pp 71–79. ACM (2010)Google Scholar
  11. 11.
    Itani, W., et al.: Privacy as a service: Privacy-aware data storage and processing in cloud computing architectures. In: Eighth IEEE international conference on dependable, autonomic and secure computing, 2009. DASC’09, pp 711–716. IEEE (2009)Google Scholar
  12. 12.
    de Montjoye, Y. A., et al.: Unique in the crowd: the privacy bounds of human mobility. Scientific reports 3 (2013)Google Scholar
  13. 13.
    Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Hu, H., et al.: Privacy-aware location data publishing. ACM Trans. Database Syst. 35(3), 17 (2010)CrossRefGoogle Scholar
  15. 15.
    Xue, M., et al.: Location diversity: enhanced privacy protection in location based services. In: loCA, ser. LNCS, vol. 5561, pp 70–87. Springer (2009)Google Scholar
  16. 16.
    Xiao, Y., et al.: Differentially private data release through multidimensional partitioning. In: Secure data management, pp 150–168 (2010)Google Scholar
  17. 17.
    Dwork, C., et al.: Calibrating noise to sensitivity in private data analysis. In: Proceedings of 3rd theory of cryptography conference, pp 265–284, New York (2006)Google Scholar
  18. 18.
    Andrés, M. E., et al.: Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC conference on computer & communications security, pp 901–914. ACM (2013)Google Scholar
  19. 19.
    Gong, Y., et al.: Identification of (near) real-time traffic congestion in the cities of australia through twitter. In: Understanding the city with urban informatics, CIKM 2015, Melbourne (2015)Google Scholar
  20. 20.
    Wang, S., et al.: Follow-Me-Not: protecting the trajectory privacy of social media users, submitted to Journal of Social Network Analysis and Mining (2015)Google Scholar
  21. 21.
    Zaldumbide, J. P., et al.: Identification and verification of real-time health events through social media. In: International conference on data science and data intensive systems, Sydney (2015)Google Scholar
  22. 22.
    Welch, V., Barlow, J., Basney, J., Marcusiu, D., Wilkins-Diehr, N.: A AAAA model to support science gateways with community accounts. Concurr. Comput. Pract. Experience 19(6), 893–904 (2007)CrossRefGoogle Scholar
  23. 23.
    Scavo, T., Welch, V.: A grid authorization model for science gateways. In: International Workshop on Grid Computing Environments (No. 3) (2007)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Richard O. Sinnott
    • 1
  • Christopher Bayliss
    • 1
  • Andrew Bromage
    • 1
  • Gerson Galang
    • 1
  • Yikai Gong
    • 1
  • Philip Greenwood
    • 1
  • Glenn Jayaputera
    • 1
  • Davis Marques
    • 1
  • Luca Morandini
    • 1
  • Ghazal Nogoorani
    • 1
  • Hossein Pursultani
    • 1
  • Muhammad Sarwar
    • 1
  • William Voorsluys
    • 1
  • Ivo Widjaja
    • 1
  1. 1.Department of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia

Personalised recommendations