Opening More Data - A New Privacy Risk Scoring Model for Open Data

Ali-Eldin, Amr; Eijk, A.M.G. Zuiderwijk-van; Janssen, Marijn

doi:10.5220/0006528301460154

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…The literature confirms that privacy and open data are strongly interconnected [5,38,46]. The concept of privacy is increasingly influenced by legislation and policy, culture, social norms and values [38].…”

Section: Preservation Of Privacymentioning

confidence: 89%

Open data policy development

Romer

Kroesen

2019

Proceedings of the 15th International Symposium on Open Collaboration

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In many countries, governments encourage municipalities to develop open data policies and subsequently open up data. Municipal open data policies are often supply-driven and not based on residents' wishes. Municipalities lack insight into residents' perspectives on opening up municipal data and often do not know how to take them into account when developing their open data policies. This paper aims to reveal residents' perspectives on municipal open data policies and provide recommendations for municipalities on how to account for them when developing future open data policies. Using Q-methodology and applying it to the municipality of Delft in the Netherlands, we elicited the perspective of four main groups of residents on the development of the municipal open data policy as follows: 1) 'the oblivious residents', 2) 'the distrustful residents', 3) 'the trusting, passive residents', and 4) 'the open data advocates'. We found that all residents considered transparency important for the quality of public administration, and that municipal transparency is currently lacking. We then provide recommendations for policy makers responsible for municipal open data policies and suggest directions for open data theory development concerning municipal open data policy.

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Section: Preservation Of Privacymentioning

confidence: 89%

Open data policy development

Romer

Kroesen

2019

Proceedings of the 15th International Symposium on Open Collaboration

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“…Calculating sub-criteria: Linguistic terms for the pairwise comparison, we are getting from Figure 5 and the corresponding fuzzy numbers are getting from the Table 2. For example, pairwise comparison of (C1.1 C1.2) is "Equal Important" and the fuzzy number of this linguistic term is (1,1,3). Normalized weight values: To find the normalized weights of C1.1 and C1.2 we used eq.…”

Section: Evaluation: Analyzing the Datasetmentioning

confidence: 99%

“…The motivation to open data by governments and private organizations have increased extensively over the last few years. The creation of transparency and accountability, to sustain citizen engagement and to enable business innovation are the main drivers to open more data [1][2][3][4]. The disclosure of data is expected to improve decision-making initiatives by both government and society [3,5].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Multi-criteria Decision Making Approach for Analyzing the Risks and Benefits of Opening Data

Luthfi

Rehena

Janssen

et al. 2018

Lecture Notes in Computer Science

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Governments are releasing their data to the public to accomplish benefits like the creation of transparency, accountability, citizen engagement and to enable business innovation. At the same time, decision-makers are reluctant to open their data due to some potential risks like misuse, sensitivity, ownership, and inaccuracy of the data. The goal of the study presented in this paper is to develop a Fuzzy Multi-Criteria Decision Making (FMCDM) approach to analyze the risks and benefits to determine the decision to open a dataset. FMCDM is chosen due to its capability to measure and weight the relative importance of the criteria. FMCDM need the weighting of criteria as input. For this Fuzzy Analytical Hierarchy Process (FAHP) is utilized by collecting input from experts' knowledge and expertise. The scores for each criterion are summed up to rank the importance of the alternatives. Four main criteria are used, e.g. data sensitivity and data ownership representing risks criteria, and data availability and data trustworthy as benefits criteria. For each criterion, there were two sub-criteria identified. Four types of decisions to open data can be made: completely open, maintain suppression, provide limited access, and remain closed. A health patient record dataset is used to illustrate the approach. In further research, we recommend to develop automated approaches that take a dataset as an input and can provide an advice.

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“…There have been works of literature introduce the methods to analyze the potential advantages and disadvantages of opening data and its consequences [6][7][8]. Methods like Bayesian-belief networks, Fuzzy multi-criteria decision-making, Decision tree analysis, and privacy risks scoring model were used to analyze the potential risks and benefits of opening data [6,7,9]. However, none of them provides insight into the comparative studies in terms of strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Methods for Deciding to Open Data

Luthfi

Janssen

2019

Lecture Notes in Business Information Processing

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Governments may have their own business processes to decide to open data, which might be supported by decision-making tools. At the same time, analyzing potential benefits, costs, risks, and other effects-adverse of disclosing data are challenging. In the literature, there are various methods to analyze the potential advantages and disadvantages of opening data. Nevertheless, none of them provides discussion into the comparative studies in terms of strengths and weaknesses. In this study, we compare three methods for disclosing data, namely Bayesian-belief networks, Fuzzy multi-criteria decisionmaking, and Decision tree analysis. The comparative study is a mechanism for further studying the development of a knowledge domain by performing a feature-by-feature at the same level of functionalities. The result of this research shows that the methods have different strengths and weaknesses. The Bayesianbelief Networks has higher accuracy in comparison, and able to construct the causal relationships of the selected variable under uncertainties. Yet, this method is more resource intensive. This study can contribute to the decision-makers and respected researchers to a better comprehend and provide recommendation related to the three methods comparison.

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Opening More Data - A New Privacy Risk Scoring Model for Open Data

Cited by 14 publications

References 8 publications

Open data policy development

Open data policy development

A Fuzzy Multi-criteria Decision Making Approach for Analyzing the Risks and Benefits of Opening Data

A Comparative Study of Methods for Deciding to Open Data

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