Article

A Problem-Based Approach for Computer-Aided Privacy Threat Identification

Authors:

Kristian Beckers,

Stephan Faβbender,

Maritta Heisel,

Rene MeisAuthors Info & Claims

APF 2012: Revised Selected Papers of the First Annual Privacy Forum on Privacy Technologies and Policy - Volume 8319

Pages 1 - 16

https://doi.org/10.1007/978-3-642-54069-1_1

Published: 10 October 2012 Publication History

Abstract

Recently, there has been an increase of reported privacy threats hitting large software systems. These threats can originate from stakeholders that are part of the system. Thus, it is crucial for software engineers to identify these privacy threats, refine these into privacy requirements, and design solutions that mitigate the threats.

In this paper, we introduce our methodology named Problem-Based Privacy Analysis ProPAn. The ProPAn method is an approach for identifying privacy threats during the requirements analysis of software systems using problem frame models. Our approach does not rely entirely on the privacy analyst to detect privacy threats, but allows a computer aided privacy threat identification that is derived from the relations between stakeholders, technology, and personal information in the system-to-be.

To capture the environment of the system, e.g., stakeholders and other IT systems, we use problem frames, a requirements engineering approach founded on the modeling of a machine system-to-be in its environment e.g. stakeholders, other software. We define a UML profile for privacy requirements and a reasoning technique that identifies stakeholders, whose personal information are stored or transmitted in the system-to-be and stakeholders from whom we have to protect this personal information. We illustrate our approach using an eHealth scenario provided by the industrial partners of the EU project NESSoS.

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Cited By

Teresa Baldassarre MSanta Barletta VCaivano DPiccinno A(2021)Integrating Security and Privacy in HCD-ScrumProceedings of the 14th Biannual Conference of the Italian SIGCHI Chapter10.1145/3464385.3464746(1-5)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3464385.3464746
Ferreyra NTessier PPedroza GHeisel M(2020)PDP-ReqLite: A Lightweight Approach for the Elicitation of Privacy and Data Protection RequirementsData Privacy Management, Cryptocurrencies and Blockchain Technology10.1007/978-3-030-66172-4_10(161-177)Online publication date: 17-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-66172-4_10
Wuyts KVan Landuyt DHovsepyan AJoosen WHaddad HWainwright RChbeir R(2018)Effective and efficient privacy threat modeling through domain refinementsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167414(1175-1178)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167414

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Information & Contributors

Information

Published In

cover image Guide Proceedings

APF 2012: Revised Selected Papers of the First Annual Privacy Forum on Privacy Technologies and Policy - Volume 8319

October 2012

214 pages

ISBN:9783642540684

Editors:
Bart Preneel
Dept. of Electrical Engineering-ESAT, KU Leuven and iMinds, Leuven-Heverlee, Belgium
,
Demosthenes Ikonomou
Information Security & Data Protection Unit, ENISA, Athens, Greece

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 10 October 2012

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Cited By

Teresa Baldassarre MSanta Barletta VCaivano DPiccinno A(2021)Integrating Security and Privacy in HCD-ScrumProceedings of the 14th Biannual Conference of the Italian SIGCHI Chapter10.1145/3464385.3464746(1-5)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3464385.3464746
Ferreyra NTessier PPedroza GHeisel M(2020)PDP-ReqLite: A Lightweight Approach for the Elicitation of Privacy and Data Protection RequirementsData Privacy Management, Cryptocurrencies and Blockchain Technology10.1007/978-3-030-66172-4_10(161-177)Online publication date: 17-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-66172-4_10
Wuyts KVan Landuyt DHovsepyan AJoosen WHaddad HWainwright RChbeir R(2018)Effective and efficient privacy threat modeling through domain refinementsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167414(1175-1178)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167414

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