Practice and Challenges of (De-)Anonymisation for Data Sharing
Authors: Alexandros Bampoulidis, Alessandro Bruni, Ioannis Markopoulos, Mihai Lupu
Personal data is a necessity in many fields for research and innovation purposes, and when such data is shared, the data controller carries the responsibility of protecting the privacy of the individuals contained in their dataset. The removal of direct identifiers, such as full name and address, is not enough to secure the privacy of individuals as shown by de-anonymisation methods in the scientific literature. Data controllers need to become aware of the risks of de-anonymisation and apply the appropriate anonymisation measures before sharing their datasets, in order to comply with privacy regulations. To address this need, we defined a procedure that makes data controllers aware of the de-anonymisation risks and helps them in deciding the anonymisation measures that need to be taken in order to comply with the General Data Protection Regulation (GDPR). We showcase this procedure with a customer relationship management (CRM) dataset provided by a telecommunications provider. Finally, we recount the challenges we identified during the definition of this procedure and by putting existing knowledge and tools into practice.
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The recent case law of the CJEU on (joint) controllership: have we lost the purpose of ‘purpose’?
Authors: Ducuing Charlotte, Schroers Jessica
‘Purpose’ is part of the definition of ‘controller’ and a cornerstone of the GDPR. Although the recent case law of the CJEU on (joint) controllership, Wirtschaftsakademie, Jehovan todistajat and Fashion ID, has been much discussed in the legal literature, little has been said about how it relates to ‘purpose’. Therefore, this paper analyses whether, in ruling about (joint) controllership, the Court (sufficiently) took into account the overall nature and functions of the notion of ‘purpose’ in the GDPR.
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Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
Authors: Peter Muellner, Dominik Kowald, Elisabeth Lex
In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction to preserve users’ privacy. We reproduce the experiments of Lin et al. on five datasets, i.e., Douban, Hetrec-MovieLens, MovieLens 1M, Ciao, and Jester. Also, we study the impact of meta learning on the accuracy of MetaMF’s recommendations. Furthermore, in our work, we acknowledge that users may have different tolerances for revealing information about themselves. Hence, in a second strand of experiments, we investigate the robustness of MetaMF against strict privacy constraints. Our study illustrates that we can reproduce most of Lin et al.’s results. Plus, we provide strong evidence that meta learning is essential for MetaMF’s robustness against strict privacy constraints.
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Why open government data initiatives fail to achieve their objectives: categorizing and prioritizing barriers through a global survey
Authors: Anneke Zuiderwijk & Mark de Reuver
Existing overviews of barriers for openly sharing and using government data are often conceptual or based on a limited number of cases. Furthermore, it is unclear what categories of barriers are most obstructive for attaining open data objectives. This paper aims to categorize and prioritize barriers for openly sharing and using government data based on many existing Open Government Data Initiatives (OGDIs).
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Creating a Taxonomy of Business Models for Data Marketplaces
Authors: Montijn Van de Ven, Antragama Ewa Abbas, Zenlin Kwee, & Mark De Reuver
Data marketplaces can fulfil a key role in realizing the data economy by enabling the commercial trading of data between organizations. Although data marketplace research is a quickly evolving domain, there is a lack of understanding about data marketplace business models. As data marketplaces are vastly different, a taxonomy of data marketplace business models is developed in this study. A standard taxonomy development method is followed to develop the taxonomy. The final taxonomy comprises of 4 meta-dimensions, 17 business model dimensions and 59 business model characteristics. The taxonomy can be used to classify data marketplace business models and sheds light on how data marketplaces are a unique type of digital platforms. The results of this research provide a basis for theorizing in this rapidly evolving domain that is quickly becoming important.
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Business Data Sharing through Data Marketplaces: A Systematic Literature Review
Authors: Antragama Ewa Abbas, Wirawan Agahari, Montijn Van De Ven, Anneke Zuiderwijk & Mark De Reuver
Data marketplaces are expected to play a crucial role in tomorrow’s data economy but hardly achieve commercial exploitation. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially neglected research topics that may contribute to advancing data marketplaces towards commercialization. This study provides an overview of the state of the art of data marketplace research. We employ a Systematic Literature Review (SLR) approach and structure our analysis using the Service-TechnologyOrganization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.
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Whitepaper on the Data Governance Act
Authors: Julie Baloup, Charlotte Ducuing, Emre Bayamlıoğlu, Aliki Benmayor, Lidia Dutkiewicz, Yuliya Miadzvetskaya, Teodora Lalova, Bert Peeters
The whitepaper offers an academic perspective to the discussion on the Data Governance Act proposal (“DGA proposal”), as adopted by the European Commission in November 2020. It contains a legal analysis of the DGA proposal and includes recommendations to amend its shortcomings. The White Paper aims to cover the full spectrum of the DGA proposal and therefore offers an in-depth analysis of its main provisions. In conclusion, the authors identify general patterns at work with the DGA proposal, namely, first, the (new) regulation of data as an object and, even more so, as an object of rights. This approach, the authors find, may contribute to exacerbate the risk of contradictions of the DGA proposal with the GDPR on the level of principles. Second, it discusses the relationship of the DGA proposal vis-à-vis the (regulation of) European data spaces and more generally its place in the two-pillars approach of the EC, between horizontal (sector-agnostic) and sectoral regulation of data. Finally, the DGA proposal is identified as a cornerstone of the new EU ‘digital sovereignty’ policy.
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Whitepaper: TRUSTS Technology – Equipping European Data Markets with Technological Innovations
Authors: Ahmad Hemid, Ohad Arnon, Stefan Gindl, Alan Barnett, Victor Mireles-Chavez
The aim of this whitepaper is to give the project stakeholders – i.e. data providers, data consumers, similar EU project consortiums, technology providers; in general the European Data Ecosystem – an overview of the technological basis of the future data market or data market federator. TRUSTS maintains an open communication policy and would like to share its own learnings from the project activities with all interested parties.
To provide a general overview of the technological developments in the project, this whitepaper explains which reference architectures TRUSTS builds on, how these have been further developed, and which innovations are necessary for the future, and thus for the achievement of the project proposal.
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Context dependent trade-offs around platform-to-platform openness: The case of the Internet of Things
Authors: Lars Mosterd, Vladimir C.M. Sobota, Geertenvan de Kaa, Aaron YiDing, Mark de Reuver
As digital platforms are dominating the digital economy, complex ecologies of platforms are emerging. While the openness of digital platforms is an important theme in platform studies, the openness between platforms has hardly been studied. This paper explores factors that affect decisions by platform owners to open their platforms to other platforms. The focus is on Internet-of-Things platforms for automotive and healthcare applications. According to the findings, platform owners make trade-offs on whether to open up on a case-by-case basis. We identify a complex array of factors relating to direct benefits and costs (e.g., revenues from selling platform data), indirect benefits (e.g., attractiveness of the focal platform to users) as well as strategic consideration (e.g., improving bargaining power towards other actors). How businesses make trade-offs on these factors depends on market-level context (e.g., maturity of the market and standards) and organizational context (e.g., strategic focus and business objectives). Our findings provide a basis for future studies on the openness between platforms, which will become increasingly important as platforms proliferate in every layer of the digital industry.
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