A Refinement Approach for the Reuse of Privacy Risk Analysis Results

De, Sourya Joyee; Métayer, Daniel Le

doi:10.1007/978-3-319-67280-9_4

Cited by 8 publications

(9 citation statements)

References 22 publications

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“…When the PIA team has identified a list of general threat scenarios, a second iteration should be performed to refine them, considering supporting assets (system’s software and equipment) that could be exploited. Then, additional threat scenarios should be considered by estimating external actors’ background information on the data subjects, leading to further harms (De and Le Métayer, 2017). An example would be a third party already having a list of names related to identity cards would gain information on health data related to such ids, so that in the end they can identify patients’ names and use the information for secondary purposes.…”

Section: A Comprehensive Privacy Impact Assessment Processmentioning

confidence: 99%

“…Although a complete method to assess privacy risks has not been proposed in existing literature, PIA practitioners could be assisted in forming risk scales by existing work of De and Le Métayer (2017), Alshammari and Simpson (2018) and Mascetti et al (2018). Also, scales proposed in ENISA’s Handbook on security of personal data processing (ENISA, 2017a, 2017b) and Commission Nationale de l’Informatique et des Libertes (CNIL, 2018) guidelines should be consulted.…”

Section: A Comprehensive Privacy Impact Assessment Processmentioning

confidence: 99%

“…However, conducting a PIA remains a complicated and demanding task for organizations processing personal data, not only because of the lack of practical guidance (Meis and Heisel, 2015; Berendt et al , 2017; Van Puijenbroek and Hoepman, 2017; De and Le Métayer, 2017) but also because of the differences of methods available. While several methods and guidelines have been published by DPAs, they follow different approaches and provide limited practical assistance on how to organize a PIA project.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating privacy impact assessment methods: guidelines and best practice

Vemou

Karyda

2019

ICS

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Purpose This paper aims to practically guide privacy impact assessment (PIA) implementation by proposing a PIA process incorporating best practices from existing PIA guidelines and privacy research. Design/methodology/approach This paper critically reviews and assesses generic PIA methods proposed by related research, data protection authorities and standard’s organizations, to identify best practices and practically support PIA practitioners. To address identified gaps, best practices from privacy literature are proposed. Findings This paper proposes a PIA process based on best practices, as well as an evaluation framework for existing PIA guidelines, focusing on practical support to PIA practitioners. Practical implications The proposed PIA process facilitates PIA practitioners in organizing and implementing PIA projects. This paper also provides an evaluation framework, comprising a comprehensive set of 17 criteria, for PIA practitioners to assess whether PIA methods/guidelines can adequately support requirements of their PIA projects (e.g. special legal framework and needs for PIA project organization guidance). Originality/value This research extends PIA guidelines (e.g. ISO 29134) by providing comprehensive and practical guidance to PIA practitioners. The proposed PIA process is based on best practices identified from evaluation of nine commonly used PIA methods, enriched with guidelines from privacy literature, to accommodate gaps and support tasks that were found to be inadequately described or lacking practical guidance.

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Section: A Comprehensive Privacy Impact Assessment Processmentioning

confidence: 99%

Section: A Comprehensive Privacy Impact Assessment Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating privacy impact assessment methods: guidelines and best practice

Vemou

Karyda

2019

ICS

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“…Table 3 illustrates the rules for assessing the value of the range of a privacy harm. These are a refinement of the rules of [6,7] to distinguish between the range of a privacy harm. The severity of a privacy harm is assessed by adding the assessed value of the intensity and the assessed value of the range, then selecting the overall value according to Table 1.…”

Section: Limitedmentioning

confidence: 99%

“…This has led to the emergence of the concept of a Privacy Impact Assessment (PIA). PIAs tend to focus more on legal and organisational aspects than on social and technical ones [6,7]. As such, it is necessary for PIA processes to be complemented by a privacy risk-assessment methodology that adopts an appropriate privacy risk model that considers organisational, legal, societal and technical aspects [2].…”

Section: Introductionmentioning

confidence: 99%

Towards an Effective Privacy Impact and Risk Assessment Methodology: Risk Assessment

Alshammari

Simpson

2018

Trust, Privacy and Security in Digital Business

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It is increasingly recognised that Privacy Impact Assessments (PIAs) play a crucial role in providing privacy protection for data subjects and in supporting risk management for organisations. However, existing PIA processes are typically not accompanied with proper guidelines and/or methodologies that sufficiently support privacy risk assessments and illustrate precisely how the core part of the PIA-a risk assessment-can be conducted. We present an approach for assessing potential privacy risks built upon a privacy risk model that considers legal, organisational, societal and technical aspects. This approach has the potential to underpin a systematic and traceable privacy risk-assessment methodology that can complement PIA processes.

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A UML Profile for Privacy-Aware Data Lifecycle Models

Alshammari

Simpson

2017

Lecture Notes in Computer Science

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Concerns over data-processing activities that may lead to privacy violations or harms have motivated the development of legal frameworks and standards to govern the processing of personal data. However, it is widely recognised that there is a disconnect between policymakers' intentions and software engineering reality. The Abstract Personal Data Lifecycle (APDL) model, which was proposed to serve as an abstract model for personal data life-cycles, distinguishes between the main operations that can be performed on personal data during its lifecycle by outlining the various distinct activities for each operation. We show how the APDL can be represented in terms of the Unified Modeling Language (UML). The profile is illustrated via a realistic case study.

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A Refinement Approach for the Reuse of Privacy Risk Analysis Results

Cited by 8 publications

References 22 publications

Evaluating privacy impact assessment methods: guidelines and best practice

Evaluating privacy impact assessment methods: guidelines and best practice

Towards an Effective Privacy Impact and Risk Assessment Methodology: Risk Assessment

A UML Profile for Privacy-Aware Data Lifecycle Models

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