Herwanto, Guntur Budi and Quirchmayr, Gerald and Tjoa, A Min (2021) A Named Entity Recognition Based Approach for Privacy Requirements Engineering. In: 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), 20-24 September 2021, Notre Dame, USA.
Full text not available from this repository. (Request a copy)Abstract
The presence of experts, such as a data protection officer (DPO) and a privacy engineer is essential in Privacy Requirements Engineering. This task is carried out in various forms including threat modeling and privacy impact assessment. The knowledge required for performing privacy threat modeling can be a serious challenge for a novice privacy engineer. We aim to bridge this gap by developing an automated approach via machine learning that is able to detect privacy-related entities in the user stories. The relevant entities include (1) the Data Subject, (2) the Processing, and (3) the Personal Data entities. We use a state-of-the-art Named Entity Recognition (NER) model along with contextual embedding techniques. We argue that an automated approach can assist agile teams in performing privacy requirements engineering techniques such as threat modeling, which requires a holistic understanding of how personally identifiable information is used in a system. In comparison to other domain-specific NER models, our approach achieves a reasonably good performance in terms of precision and recall. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 17 |
Uncontrolled Keywords: | Agile manufacturing systems; Data privacy; Agile development; Automated approach; Impact assessments; Named entity recognition; Privacy requirement engineering; Privacy requirements; Recognition models; Requirement engineering; Threat modeling; User stories; Requirements engineering |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 01 Nov 2024 02:15 |
Last Modified: | 01 Nov 2024 02:15 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8493 |