Identifying privacy risks in distributed data services: A model-driven approach
Identifying privacy risks in distributed data services: A model-driven approach
Abstract: Online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data. It is crucial that such systems are engineered in a privacy-aware manner in order to satisfy both the privacy requirements of the user, and the legal privacy regulations that the system operates under. How can system developers be better supported to create privacy-aware systems and help them to understand and identify privacy risks? Model-Driven Engineering (MDE) offers a principled approach to engineer systems software. The capture of shared domain knowledge in models and corresponding tool support can increase the developers' understanding. In this paper, we argue for the application of MDE approaches to engineer privacy-aware systems. We present a general purpose privacy model and methodology that can be used to analyse and identify privacy risks in systems that comprise both access control and data pseudonymization enforcement technologies. We evaluate this method using a case-study based approach and show how the model can be applied to engineer privacy-aware systems and privacy
policies that reduce the risk of unintended disclosure.
1513 - 1518
Grace, Paul
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Burns, Daniel
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Neumann, Geoffrey
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Pickering, Brian
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Melas, Panagiotis
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Surridge, Michael
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23 July 2018
Grace, Paul
b48ef8f2-b116-48ce-b774-4d43808cc02f
Burns, Daniel
40b9dc88-a54a-4365-b747-4456d9203146
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Melas, Panagiotis
bf7a965b-691f-4380-96d1-f2f8eb319c89
Surridge, Michael
3bd360fa-1962-4992-bb16-12fc4dd7d9a9
Grace, Paul, Burns, Daniel, Neumann, Geoffrey, Pickering, Brian, Melas, Panagiotis and Surridge, Michael
(2018)
Identifying privacy risks in distributed data services: A model-driven approach.
In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).
IEEE.
.
(doi:10.1109/ICDCS.2018.00157).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Abstract: Online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data. It is crucial that such systems are engineered in a privacy-aware manner in order to satisfy both the privacy requirements of the user, and the legal privacy regulations that the system operates under. How can system developers be better supported to create privacy-aware systems and help them to understand and identify privacy risks? Model-Driven Engineering (MDE) offers a principled approach to engineer systems software. The capture of shared domain knowledge in models and corresponding tool support can increase the developers' understanding. In this paper, we argue for the application of MDE approaches to engineer privacy-aware systems. We present a general purpose privacy model and methodology that can be used to analyse and identify privacy risks in systems that comprise both access control and data pseudonymization enforcement technologies. We evaluate this method using a case-study based approach and show how the model can be applied to engineer privacy-aware systems and privacy
policies that reduce the risk of unintended disclosure.
Text
icdcs_iti_2018
- Accepted Manuscript
More information
Submitted date: 15 March 2018
Accepted/In Press date: 19 April 2018
e-pub ahead of print date: 23 July 2018
Published date: 23 July 2018
Venue - Dates:
IEEE International Conference on Distributed Computing Systems, , Vienna, Austria, 2018-07-02 - 2018-07-05
Identifiers
Local EPrints ID: 419998
URI: http://eprints.soton.ac.uk/id/eprint/419998
ISSN: 2575-8411
PURE UUID: d6ea4218-2435-4407-af25-f61721365318
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Date deposited: 25 Apr 2018 16:30
Last modified: 26 Aug 2024 01:32
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Contributors
Author:
Paul Grace
Author:
Daniel Burns
Author:
Geoffrey Neumann
Author:
Panagiotis Melas
Author:
Michael Surridge
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