IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v26y2024i6d10.1007_s10796-021-10116-w.html
   My bibliography  Save this article

Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

Author

Listed:
  • Wang Ren

    (Sichuan University
    China Information Technology Security Evaluation Center)

  • Xin Tong

    (China Information Technology Security Evaluation Center)

  • Jing Du

    (China Information Technology Security Evaluation Center)

  • Na Wang

    (China Information Technology Security Evaluation Center)

  • Shancang Li

    (University of the West of England)

  • Geyong Min

    (University of Exeter)

  • Zhiwei Zhao

    (University of Electronic Science and Technology of China)

Abstract

The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

Suggested Citation

  • Wang Ren & Xin Tong & Jing Du & Na Wang & Shancang Li & Geyong Min & Zhiwei Zhao, 2024. "Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation," Information Systems Frontiers, Springer, vol. 26(6), pages 2227-2238, December.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:6:d:10.1007_s10796-021-10116-w
    DOI: 10.1007/s10796-021-10116-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-021-10116-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-021-10116-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Anjee Gorkhali & Ling Li & Asim Shrestha, 2020. "Blockchain: a literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(3), pages 321-343, July.
    2. Luc Rocher & Julien M. Hendrickx & Yves-Alexandre de Montjoye, 2019. "Estimating the success of re-identifications in incomplete datasets using generative models," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2021. "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions," JRFM, MDPI, vol. 14(2), pages 1-29, January.
    2. Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.
    3. Ratul Das Chaudhury & Chongwoo Choe, 2023. "Digital Privacy: GDPR and Its Lessons for Australia," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(2), pages 204-220, June.
    4. Marc Fadel & Julien Petot & Pierre-Antoine Gourraud & Alexis Descatha, 2024. "Flexibility of a large blindly synthetized avatar database for occupational research: Example from the CONSTANCES cohort for stroke and knee pain," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-9, July.
    5. Rehse, Dominik & Tremöhlen, Felix, 2020. "Fostering participation in digital public health interventions: The case of digital contact tracing," ZEW Discussion Papers 20-076, ZEW - Leibniz Centre for European Economic Research.
    6. Liebe, Andrea & Kroon, Peter & Wiewiorra, Lukas, 2024. "Data Access, Data Sharing und Privacy," WIK Discussion Papers 527, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH.
    7. Tesary Lin & Sanjog Misra, 2022. "Frontiers: The Identity Fragmentation Bias," Marketing Science, INFORMS, vol. 41(3), pages 433-440, May.
    8. Rashi Saxena & E. Gayathri & Lalitha Surya Kumari, 2023. "Semantic analysis of blockchain intelligence with proposed agenda for future issues," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 34-54, March.
    9. Atabey, Ayça & Pothong, Kruakae & Livingstone, Sonia, 2023. "Glossary of terms relating to children’s digital lives," LSE Research Online Documents on Economics 119728, London School of Economics and Political Science, LSE Library.
    10. German Data Forum RatSWD (ed.), 2020. "Data collection using new information technology," RatSWD Output Series, German Data Forum (RatSWD), volume 6, number 6-6en, November.
    11. Xueling Li & Xiaoyan Zhang & Yuan Liu & Yuanying Mi & Yong Chen, 2022. "The impact of artificial intelligence on users' entrepreneurial activities," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 597-608, May.
    12. Jianjing Qu & Yanan Zhao & Yongping Xie, 2022. "Artificial intelligence leads the reform of education models," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 581-588, May.
    13. Jeongwook Lee & Joon Jin Song & Yongku Kim & Jung In Seo, 2020. "Estimation and Prediction of Record Values Using Pivotal Quantities and Copulas," Mathematics, MDPI, vol. 8(10), pages 1-16, October.
    14. Chu-Chi Kuo & Joseph Z. Shyu, 2021. "A Cross-National Comparative Policy Analysis of the Blockchain Technology between the USA and China," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    15. George SUCIU & Mari Anais SACHIAN & Grigor PARANGONI & Maria NICULAE & Robert FLORESCU, 2022. "Blockchain applications in smart grid," Smart Cities International Conference (SCIC) Proceedings, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 10, pages 387-394, November.
    16. Miren Gutierrez & John Bryant, 2022. "The Fading Gloss of Data Science: Towards an Agenda that Faces the Challenges of Big Data for Development and Humanitarian Action," Development, Palgrave Macmillan;Society for International Deveopment, vol. 65(1), pages 80-93, March.
    17. Nien-Ping Chen & Kao-Yi Shen & Chiung-Ju Liang, 2021. "Hybrid Decision Model for Evaluating Blockchain Business Strategy: A Bank’s Perspective," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    18. Se-Ra Oh & Young-Duk Seo & Euijong Lee & Young-Gab Kim, 2021. "A Comprehensive Survey on Security and Privacy for Electronic Health Data," IJERPH, MDPI, vol. 18(18), pages 1-48, September.
    19. Toufaily, Elissar & Zalan, Tatiana, 2024. "In blockchain we trust? Demystifying the “trust” mechanism in blockchain ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    20. Sevgi Arca & Rattikorn Hewett, 2021. "Analytics on Anonymity for Privacy Retention in Smart Health Data," Future Internet, MDPI, vol. 13(11), pages 1-20, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:26:y:2024:i:6:d:10.1007_s10796-021-10116-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.