Data Marketplace Research Articles

Stuck in the middle with you: Conceptualizing data intermediaries and data intermediation services

Data intermediation services are a novel class of services mitigating various aspects of inter-organizational data sharing. Facilitating data intermediation services is a pivotal activity that leverages the benefits of the data economy. These services are usually provided by so-called data intermediaries, who organize data sharing between one or multiple data provider(s) and data consumer(s). Against the background of prominent legislation, these data intermediaries (e.g., data marketplaces or data trusts) receive increasing attention in research, practice, and politics. For instance, the Data Governance Act regulates data intermediation services, prompting organizations sharing data to comply with various regulations, including registration with government bodies or acquiring a trusted label. However, to date, little research has been done about the nature of data intermediation services, motivating us to tackle this issue in a two-fold strategy. First, we derive a visualization of a data intermediary and concept subsuming its potential functions. Second, we explore archetypical configurations of co-existing services of data intermediaries. With this, we contribute to understanding the data intermediaries as a concept and enrich this understanding with more detailed service configurations.

Creating a Health Data Marketplace for the Digital Health Era

With the advent of the General Data Protection Regulation (GDPR) of the EU and the development of technologies like blockchain and distributed ledger technologies (DLT), it is now possible to create a new paradigm with a shared economic model where financial Incentivization will be the main driver for data sharing. This can be achieved by setting up a digital health data marketplace (DHDM). Speakers on s podcast authored a paper in BTHY journal entitled “From Sharing to Selling: Challenges and Opportunities of Establishing a Digital Health Data Marketplace Using Blockchain Technologies,” https://doi.org/10.30953/bhty.v5.184 Their ongoing work is discussed for the DHDM operation outlined along with current developments and future work. Questions addressed are below: What inspired the authors to explore the use of blockchain technology in healthcare data sharing and monetization? What are the key challenges and opportunities identified in establishing a digital health data marketplace using blockchain technologies? What are the potential socioeconomic impacts of a digital health data marketplace on patients, healthcare providers, and researchers, and how do you see the economics of health data developing into dynamic systems that will reflect in the processes of care delivery and management? What are the next steps for the research? Are there ongoing projects or collaborations the audience can expect to be excited about?

Creating a Health Data Marketplace for the Digital Health Era

The author posits that the establishment of a blockchain-based health data marketplace where EHR, IoMT, and wearable-derived data can be monetized by selling it to data consumers like medical professionals, researchers, regulators, third-party (e.g., AI service providers) and policymakers can solve this data centralization problem.

An edge computing and distributed ledger technology architecture for secure and efficient transportation

Intelligent Transportation Systems (ITS) faces significant challenges in achieving its goal of sustainable and efficient transportation. These challenges include real-time data processing bottlenecks caused by high communication latency and security vulnerabilities related to centralized data storage. We propose a novel architecture that leverages Edge Computing and Distributed Ledger Technology (DLT) to address these concerns. Edge computing pushes cloud services, such as vehicles and roadside units, closer to the data source. This strategy reduces latency and network congestion. DLT provides a secure, decentralized platform for storing and sharing ITS data. Its tamper-proof nature ensures data integrity and prevents unauthorized access. Our architecture utilizes these technologies to create a decentralized platform for ITS data management. This platform facilitates secure processing, storage, and data exchange from various sources in the transportation network. This paper delves deeper into the architecture, explaining its essential components and functionalities. Additionally, we explore its potential applications and benefits for ITS. We describe a case study focusing on a data marketplace system for connected vehicles to assess the architecture’s effectiveness. The simulation results show an average latency reduction of 83.35% for publishing and 87.57% for purchasing datasets compared to the cloud architecture. Additionally, transaction processing speed improved by 18.73% and network usage decreased by 96.67%. The proposed architecture also achieves up to 99.61% reduction in mining centralization.

A privacy-aware authentication and usage-controlled access protocol for IIoT decentralized data marketplace

Data is ubiquitous, powerful and valuable today. With vast instalments of Industrial Internet-of-Things (IIoT) infrastructure, data is in abundance albeit sitting in organizational silos. Data Marketplaces have emerged to allow monetization of data by trading it with interested buyers. While centralized marketplaces are common, they are controlled by few and are non-transparent. Decentralized data marketplaces allow the democratization of rates, trading terms and fine control to participants. However, in such a marketplace, ensuring privacy and security is crucial. Existing data exchange schemes depend on a trusted third party for key management during authentication and rely on a ‘one-time-off’ approach to authorization. This paper proposes a user-empowered, privacy-aware, authentication and usage-controlled access protocol for IIoT data marketplace. The proposed protocol leverages the concept of Self-Sovereign Identity (SSI) and is based on the standards of Decentralized Identifier (DID) and Verifiable Credential (VC). DIDs empower buyers and give them complete control over their identities. The buyers authenticate and prove claims to access data securely using VC. The proposed protocol also implements a dynamic user-revocation policy. Usage-controlled based access provides secure ongoing authorization during data exchange. A detailed performance and security analysis is provided to show its feasibility.

DataPrice: An Interactive System for Pricing Datasets in Data Marketplaces

With the flourishing of data-driven applications, data marketplaces, which can dramatically facilitate data utilization, have emerged recently. However, determining the appropriate price for datasets presents a significant challenge due to the intangible nature of data. DataPrice alleviates the challenge by providing an interactive system based on a pricing model trained on real datasets collected from commercial data marketplaces. The pricing model can estimate an appropriate price according to the dataset description as a reference for both data sellers and data buyers. By leveraging Shapley values to evaluate the contribution of each attribute in metadata on the estimated price, DataPrice offers an explanation of the pricing process in a text form to enhance user trust.

Untapping the potential of mobile location data: The opportunities and challenges for retail analytics

Smartphone technology has created a burgeoning mobile location data (MLD) marketplace. MLD has been adopted by many retailers to track consumer behaviours at a granular level across space and time. This paper provides a critical empirical case study approach to generate spatiotemporal MLD-based insights using raw smartphone data at five super-regional shopping centres. The analysis and visualization of MLD-based metrics provide the basis to rethink how we approach retail analytics and location decision-making. Many challenges need to be addressed to leverage the full potential of MLD, including data bias, spatial and temporal accuracy and significant consumer privacy concerns.

Special Issue on Data Economy and Data Marketplaces

Special Issue on Data Economy and Data Marketplaces

A Socially Optimal Data Marketplace With Differentially Private Federated Learning

A Socially Optimal Data Marketplace With Differentially Private Federated Learning

Stochastic Resource Optimization for Metaverse Data Marketplace by Leveraging Quantum Neural Networks

Stochastic Resource Optimization for Metaverse Data Marketplace by Leveraging Quantum Neural Networks

A Decentralized Private Data Marketplace using Blockchain and Secure Multi-Party Computation

Big data has proven to be a very useful tool for companies and users, but companies with larger datasets have ended being more competitive than the others thanks to machine learning or artificial intelligence. Secure multi-party computation (SMPC) allows the smaller companies to jointly train arbitrary models on their private data while assuring privacy, and thus gives data owners the ability to perform what are currently known as federated learning algorithms. Besides, with a blockchain it is possible to coordinate and audit those computations in a decentralized way. In this document, we consider a private data marketplace as a space where researchers and data owners meet to agree the use of private data for statistics or more complex model trainings. This document presents a candidate architecure for a private data marketplace by combining SMPC and a public, general-purpose blockchain. Such a marketplace is proposed as a smart contract deployed in the blockchain, while the privacy preserving computation is held by SMPC.

A blockchain-based healthcare data marketplace: prototype and demonstration.

This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

DataMesh+: A Blockchain-Powered Peer-to-Peer Data Exchange Model for Self-Sovereign Data Marketplaces.

In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model's acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance.

Blockchain Support for a FAIR Research Data Marketplace

Blockchain Support for a FAIR Research Data Marketplace

Social network node pricing based on graph autoencoder in data marketplaces

Data have become a valuable digital resource. It has in turn precipitated the emergence of big data marketplaces. For social network date in the marketplaces, each node should be priced according to its influence. The key challenge is that deep learning based pricing models require initial cascade graphs as inputs to predict influence, which cannot be obtained while pricing nodes. Furthermore, node pricing must enhance purchase intentions while being consistent with their influence. To address these challenges, a node-pricing framework is proposed, in which market price is determined based on the predicted influence. In this framework, corrections are performed by using a graph autoencoder (GAE). The corrections are used to augment the neighborhood subgraph and facilitate the extraction of valid sequence features, which are then used to predict influence. An approximate Shapley value for node influence is used to evaluate the price of the nodes. A multi-perspective pricing approach is further investigated, where consumer utility and the approximate Shapley value for influence are the objectives. An inflection point is chosen on the Pareto frontier to select a price that enhances consumer utility. Extensive experiments were conducted on two real-world social network datasets. The results indicate that our performance is higher than DeepCas by 10.38% in Twitter and 9.64% in Weibo . The price output by our framework is consistent with the nodes’ social marketing value while maximizing consumer utility.

Functional suitability assessment of smart contracts: A survey and first proposal

AbstractBlockchain is a cross‐cutting technology allowing interactions among untrusted entities in a distributed manner without the need for involving a trusted third party. Smart contracts (i.e., programs running on the blockchain) enabled organizations to envision and implement solutions to real‐world problems in less cost and time. Given the immutability of blockchain and the lack of best practices for properly designing and developing smart contracts, it is crucial to assure smart contract quality before deployment. With the help of an exploratory survey involving developers and researchers, this paper identifies the practices and tools used to develop, implement, and evaluate smart contracts. The survey received 55 valid responses. Such responses indicate that (i) inefficiencies may occur during the development cycle of a smart contract, especially regarding requirements specification, design, and testing phases, and (ii) the lack of a shared standard to evaluate the functional quality of implemented smart contracts. To start coping with these issues, the adoption of functional suitability assessment measures recommended by the ISO/IEC 25000 standard, widely used in software engineering, is proposed by adapting them to the context of smart contracts. Through some examples, the manuscript also illustrates how to measure the functional completeness and correctness of smart contracts. The proposed procedure to measure smart contract functional suitability brings advantages to both developers and users of decentralized finance or non‐fungible tokens platforms, data marketplaces, or shipping and real estate services, just to mention a few. In particular, it helps (i) better outline the responsibilities of smart contracts, (ii) uncover errors and deficiencies of smart contracts in the early stages, and (iii) ensure that the established requirements are met.

Introducing the enterprise data marketplace: a platform for democratizing company data

In this big data era, multitudes of data are generated and collected which contain the potential to gain new insights, e.g., for enhancing business models. To leverage this potential through, e.g., data science and analytics projects, the data must be made available. In this context, data marketplaces are used as platforms to facilitate the exchange and thus, the provisioning of data and data-related services. Data marketplaces are mainly studied for the exchange of data between organizations, i.e., as external data marketplaces. Yet, the data collected within a company also has the potential to provide valuable insights for this same company, for instance to optimize business processes. Studies indicate, however, that a significant amount of data within companies remains unused. In this sense, it is proposed to employ an Enterprise Data Marketplace, a platform to democratize data within a company among its employees. Specifics of the Enterprise Data Marketplace, how it can be implemented or how it makes data available throughout a variety of systems like data lakes has not been investigated in literature so far. Therefore, we present the characteristics and requirements of this kind of marketplace. We also distinguish it from other tools like data catalogs, provide a platform architecture and highlight how it integrates with the company’s system landscape. The presented concepts are demonstrated through an Enterprise Data Marketplace prototype and an experiment reveals that this marketplace significantly improves the data consumer workflows in terms of efficiency and complexity. This paper is based on several interdisciplinary works combining comprehensive research with practical experience from an industrial perspective. We therefore present the Enterprise Data Marketplace as a distinct marketplace type and provide the basis for establishing it within a company.

Call for Papers: IEEE Internet Computing Special Issue on Data Economy and Data Marketplaces

Call for Papers: IEEE Internet Computing Special Issue on Data Economy and Data Marketplaces

Industrial data monetization: A blockchain-based industrial IoT data trading system

The Internet of Things (IoT) devices utilized in manufacturing industries produce vast volumes of valuable data that can revolutionize productivity and sustainability. While existing centralized data marketplaces facilitate data trading, they are often marred by trust issues, a single point of failure, and significant security and privacy vulnerabilities. Addressing these critical shortcomings, this paper introduces a blockchain-based industrial data trading system that not only ensures secure and transparent data trading but also offers advantages over conventional systems. Unlike traditional marketplaces, our proposed system emphasizes trustworthiness by mitigating third-party risks, enhancing data integrity through decentralized storage, and employing graph technology for efficient blockchain querying. Further, with integrated access control, our system elevates security standards. Preliminary evaluations reveal the system’s potential to offer a more secure, transparent, auditable, and trustworthy data trading environment, distinctly outpacing the capabilities of current marketplaces.

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.