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CASTLEGUARD : anonymised data streams with guaranteed differential privacy
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Robinson, Alistair, Brown, Frederick, Hall, Nathan, Jackson, Alexander, Kemp, Graham and Leeke, Matthew (2021) CASTLEGUARD : anonymised data streams with guaranteed differential privacy. In: 18th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'20), Calgary, Canada, 17-22 Aug 2020. Published in: 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) ISBN 9781728166100. doi:10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00102
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WRAP-CASTLEGUARD-Anonymised-data-streams-Leeke-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1580Kb) | Preview |
Official URL: https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSci...
Abstract
Data streams are commonly used by data controllers to outsource the processing of real-time data to third-party data processors. Data protection legislation and best practice in data management support the view that data controllers are responsible for providing a guarantee of privacy for user data contained within published data streams. Continuously Anonymising STreaming data via adaptive cLustEring (CASTLE) is an established method for anonymising data streams with a guarantee of k-anonymity. However, k-anonymity has been shown to be a weak privacy guarantee that has vulnerabilities in practical applications. In this paper we propose Continuously Anonymising STreaming data via adaptive cLustEring with GUAR-anteed Differential privacy (CASTLEGUARD), a data stream anonymisation algorithm that provides a reliable guarantee of k-anonymity, l-diversity and differential privacy to data subjects. We analyse CASTLEGUARD to show that, through safe k-anonymisation and β-sampling, the proposed approach satisfies differentially private k-anonymity. Further, we demonstrate the efficacy of the approach in the context of machine learning, presenting experimental analysis to demonstrate that it can be used to protect the individual privacy of users whilst maintaining the utility of a data stream.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Big data, Big data -- Security measures, Streaming technology (Telecommunications) , Data mining, Data protection, Computer networks -- Security measures | ||||||
| Journal or Publication Title: | 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781728166100 | ||||||
| Official Date: | 11 November 2021 | ||||||
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| DOI: | 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00102 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Description: | All authors are from Warwick Department of Computer Science |
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | 18th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC'20) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Calgary, Canada | ||||||
| Date(s) of Event: | 17-22 Aug 2020 | ||||||
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