Using Blockchain Technology to Manage Membership and Legal Contracts in a Distributed Data Market

Schlarb, Sven; Karl, Roman; King, Ross; Lampoltshammer, Thomas J.; Thurnay, Lörinc; Ivanschitz, Bernd-Peter; Mireles, Víctor

doi:10.1109/sds.2019.8768612

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…This facet entails properties such as featurerichness (i.e., how many dimensions are in the data) [101] and truthfulness (i.e., the extent to which the data corresponds with reality) [118]. Finally, data quality has a contextbased facet [119], which includes the ease with which it can be integrated with other data [34], the relevance of the data for the data consumer, as well as (proof of) data provenance (i.e., the origin of the data) [10], [13].…”

Section: Data Quality Assessmentmentioning

confidence: 99%

“…In the wake of these trends, many data markets have sprung up, facilitating data exchange between data providers and data consumers. These data markets capitalise on the increased appreciation of the value of data, catering to different domains (e.g., IoT [7], medical data [8] manufacturing data [9]) and contexts (e.g., national data [10], [11]). Therefore, it is not surprising that the scientific community has taken an interest in the phenomenon of data markets as well: as fig.…”

Section: Introductionmentioning

confidence: 99%

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Data Market Design: A Systematic Literature Review

2022

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Data markets are platforms that provide the necessary infrastructure and services to facilitate the exchange of data products between data providers and data consumers from different environments. Over the last decade, many data markets have sprung up, capitalising on the increased appreciation of the value of data and catering to different domains. In this work, we analyse the existing body of scientific literature on data markets to provide the first comprehensive overview of research into the design of data markets, regardless of scientific background or application domain. In doing so, we contribute to the field in several ways: 1) We present an overview of the state of the art in academic research on data markets and compare this with existing market trends to identify potential gaps. 2) We identify important application domains and contexts where data markets are being put into practice. 3) Finally, we provide taxonomies of both design problems for data markets and the solutions that are being investigated to address them. We conclude our work by identifying common types of data markets and corresponding best practices for designing them. The outcome of this work is intended to serve as a starting point for software architects and engineers looking to design data markets.

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Section: Data Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Market Design: A Systematic Literature Review

2022

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“…Similar initiatives also exist internationally, such as the IOTA Marketplace [7], the planned Food Data Market [8] or the targeted and GDPR-compliant search for patient data in the H2020 project FeatureCloud [9]. All these initiatives and concepts are strictly centralised and service oriented, i.e., a central operator operates a central market (also with distributed evaluation via block chains or distributed payment via Smart Contracts [10]). This does not necessarily mean that the data traded through these markets are stored on a central location; some concepts let the data reside with the data owners, and solely provide the brokering but, still, the service is centralised to some extent.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Furthermore, works on security in data markets, such as [24], typically focus on the privacy aspects of the people involved in the traded data sets, which is, of course, an important topic but does not cover the whole problem field of secure data markets. Other approaches focus more on the integrity of the market and the involved data, proposing, for example, blockchain-based solutions to the problem [10,25]. Related topics, such as data lakes [26], have been studied, for example, with regard to counterfeit protection in the event of data being changed by externally involved third parties [27]; however, these works relate more to global than internal markets with respect to their setup and especially the security issues surrounding them.…”

Section: Background and Related Workmentioning

confidence: 99%

Secure Internal Data Markets

2021

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The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (e.g., between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption.

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“…de la Vega et al [16] proposed a peer-to-peer distributed data trading model with an IoT context broker to operate blockchain as ledger management and intermediates the participants. Schlarb et al [17] proposed a distributed data market model with a market platform (i.e., data broker) to manage the market participants and various metadata to operate data trading transactions. Chen et al [19] proposed a blockchain-based data trading approach for the Internet of Vehicles.…”

Section: Introductionmentioning

confidence: 99%

Deposit Decision Model for Data Brokers in Distributed Personal Data Markets Using Blockchain

Park

Choi

et al. 2021

IEEE Access

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Since personal data become a valuable asset for IT industries, the necessity of data ecosystems with personal data trading methodologies is continuously increased, and the concept of data broker is now widely used in the market. Moreover, utilizing the advantages of blockchain, distributed personal data markets have recently been considered because all participants are behaving selfishly for their profits in the markets. Since the participants may behave maliciously, it is necessary to prevent such behavior, and one of the considered approaches is putting additional deposits as a penalty into blockchain smart contracts. Since data brokers give various advantages to maintain personal data trading markets, many studies have considered both blockchain and data brokers. However, those studies have mainly focused on putting deposits into data buyers/sellers but not data brokers. However, since data brokers are also major players in the market, it is necessary to consider deposit decision models for data brokers. Hence, this paper proposes a deposit decision model for data brokers in distributed personal data markets using blockchain. Particularly, this paper proposes a profit model with deposits depending on their behavior for handling contracts and a credit level model that puts less amount of deposits for a data broker with a higher credit level to motivate the data brokers' truthful behavior. With the analysis of the proposed models, this paper shows that the models are feasible to motivate data brokers' truthful behavior by allocating deposits for not only a large enough penalty but also a fair enough incentive.

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Using Blockchain Technology to Manage Membership and Legal Contracts in a Distributed Data Market

Cited by 4 publications

References 9 publications

Data Market Design: A Systematic Literature Review

Data Market Design: A Systematic Literature Review

Secure Internal Data Markets

Deposit Decision Model for Data Brokers in Distributed Personal Data Markets Using Blockchain

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