Towards Privacy Policy Conceptual Modeling

Krasnashchok, Katsiaryna; Mustapha, Majd; Bassit, Anas Al; Skhiri, Sabri

doi:10.1007/978-3-030-62522-1_32

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…To this end, there is a clear need to represent usage policies, both for user consent and analyst data handling, in a clear and concise manner. In recent years, a number of ontologies has been proposed to represent usage policies, e.g., DPV [54], PrOnto [52], SPECIAL [8] and SAVE [38], among others.…”

Section: Usage Policy Representationmentioning

confidence: 99%

“…SPECIAL, 27 a European H2020 project, have developed the SPECIAL Policy Language [8] around the recent OWL2 standard with the goal to adequately trade off expressiveness and computational complexity for usage policy checking. Finally, there is the SAVE ontology [38], which integrates and aligns to DPV [54], ODRL [31], and ORCP [36] to provide a fine-grained representation of privacy policies. While in essence any of these vocabularies can be used for usage policy representation in our use case, none of them is community-based and geared towards open standards, unlike DPV [54].…”

Section: Consent Managementmentioning

confidence: 99%

“…On the other hand, the research on representation and compliance checking for user consent and personal data handling also gained traction. Semantic Web technologies, with ontologies and reasoners as key resources, have been proposed to facilitate representing [38,52,54] and automated compliance checking [6,23] on usage policy, which are key to enable consent-check mechanisms for privacy-preserving data analysis.…”

Section: Introductionmentioning

confidence: 99%

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Semantic-enabled architecture for auditable privacy-preserving data analysis

Ekaputra

Ekelhart

Mayer

et al. 2024

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Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data centrally, which means losing the potential of data analytics and learning from aggregated user data. To enable organisations to leverage the full-potential of the collected personal data, two main technical challenges need to be addressed: (i) organisations must preserve the privacy of individual users and honour their consent, while (ii) being able to provide data and algorithmic governance, e.g., in the form of audit trails, to increase trust in the result and support reproducibility of the data analysis tasks performed on the collected data. Such an auditable, privacy-preserving data analysis is currently challenging to achieve, as existing methods and tools only offer partial solutions to this problem, e.g., data representation of audit trails and user consent, automatic checking of usage policies or data anonymisation. To the best of our knowledge, there exists no approach providing an integrated architecture for auditable, privacy-preserving data analysis. To address these gaps, as the main contribution of this paper, we propose the WellFort approach, a semantic-enabled architecture for auditable, privacy-preserving data analysis which provides secure storage for users’ sensitive data with explicit consent, and delivers a trusted, auditable analysis environment for executing data analytic processes in a privacy-preserving manner. Additional contributions include the adaptation of Semantic Web technologies as an integral part of the WellFort architecture, and the demonstration of the approach through a feasibility study with a prototype supporting use cases from the medical domain. Our evaluation shows that WellFort enables privacy preserving analysis of data, and collects sufficient information in an automated way to support its auditability at the same time.

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Section: Usage Policy Representationmentioning

confidence: 99%

Section: Consent Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic-enabled architecture for auditable privacy-preserving data analysis

Ekaputra

Ekelhart

Mayer

et al. 2024

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“…Year Mention Use Extension Contributed Domain [11] 2020 X Health [53] 2020 X Media [7,54,81] 2020 X [12,47,51,71] 2020 X [10] 2020 X Health [15,60,74] 2020 X X [17,55,29] 2021 X Health [37,6,76,62,39] 2021 X [72,20,16,73,52] 2021 X [34] 2021 X Smart products [42] 2021 X X [44,46,43,75,59,66,22] 2022 X [21] 2022 X X [69,70,57,23] 2022 X X [5] 2022 X Health [25,14,13,32,50] 2022 X [4] 2022 X IoT [40] 2022 X Health [58,…”

Section: Workmentioning

confidence: 99%

“…In terms of applications to specific use cases, there is a body of work focusing on providing tools for auditing and GDPR compliance evaluation [71,72,79], on data minimisation [34], as well as on the documentation and annotation of privacy policies [51,52,47,38] and privacy preferences [4]. Moreover, ML models were trained with DPV's taxonomies to identify personal data processing activities in code repositories [80,41] and textual datasets [32,33].…”

Section: Workmentioning

confidence: 99%

Data Privacy Vocabulary (DPV) - Version 2

Pandit,

Esteves,

Krog

et al. 2024

Preprint

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The Data Privacy Vocabulary (DPV), developed by the W3C Data Privacy Vocabularies and Controls Community Group (DPVCG), enables the creation of machine-readable, interoperable, and standards-based representations for describing the processing of personal data. The group has also published extensions to the DPV to describe specific applications to support legislative requirements such as the EU's GDPR. The DPV fills a crucial niche in the state of the art by providing a vocabulary that can be embedded and used alongside other existing standards such as W3C ODRL, and which can be customised and extended for adapting to specifics of use-cases or domains. This article describes the version 2 iteration of the DPV in terms of its contents, methodology, current adoptions and uses, and future potential. It also describes the relevance and role of DPV in acting as a common vocabulary to support various regulatory (e.g. EU's DGA and AI Act) and community initiatives (e.g. Solid) emerging across the globe.

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