The application of blockchain technology in data trading: a systematic review

Introduction

With the rapid development of cloud computing and Internet-of-Things technologies, the volume of global data generation is growing exponentially, and human society has fully entered the big data era (Botta et al., 2016). Against this backdrop, data sharing and trading are becoming an inevitable trend for the development of the digital economy, and their market potential has been widely concerned by the academic and industrial circles (Sutherland & Jarrahi, 2018). The value of data as a new type of production factor is being deeply explored by various industries (Lee, 2017), and the construction of data trading platforms has become an important way to achieve data circulation, giving rise to diversified models such as autonomous data sales, data processing and resale, and platform-based services (Liang et al., 2018; Peters, 2023).

The current mainstream data trading platform service models are mainly divided into two categories: the custody-based trading model and the aggregation-based trading model. In the custody-based model, institutions fully transfer their data assets to a centralized database. The platform has complete control over the subsequent data trading and application, and the original data providers are completely separated from the trading process (Wang et al., 2018; Liu & Lin, 2023). For example, the choice of trading frequency and trading partners depends entirely on the platform’s unilateral decision-making, and the protection of data rights essentially relies on the platform’s credit endorsement. In contrast, the aggregation-based model dynamically connects multiple data sources through application programming interfaces. Business institutions retain data management rights and only interact in real-time via the platform when a query request is triggered (Xiong & Xiong, 2019). Although this model appears to maintain distributed control of data, an in-depth analysis of the data flow mechanism reveals that the platform can still gradually gain control over data by accumulating transaction logs, eventually turning into a de facto custody-based model (Yu et al., 2020).

However, the centralized management mechanism mentioned above is becoming a significant barrier to data circulation, causing multiple systemic risks. First, the lack of trust among trading parties leads to fairness disputes, especially in complex trading scenarios with multiple participants (Chen et al., 2019a). Second, centralized platforms’ data retention practices increase the risks of privacy breaches and data tampering (Zhao et al., 2019; Yang et al., 2020). Third, single points of failure and low transparency severely affect the availability and credibility of transactions (He et al., 2019; Hu et al., 2021). Recent research further indicates that insufficient data availability and unclear rights ownership have become key bottlenecks hindering the market’s large-scale development (Yu et al., 2024; Bauer-Hänsel et al., 2024). It is clear that relying solely on platform promises cannot create a trustworthy trading environment. There is an urgent need to rebuild the data rights protection system through technical mechanisms (Li et al., 2018; Janssen et al., 2020).

To address these challenges, decentralized trading models based on blockchain and smart contracts have become a research focus. Blockchain technology, following the steam engine, electricity, and the internet, is regarded as the next-generation disruptive technology and a world-class computing paradigm. It represents a revolutionary technological advancement, serving as a completely new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Characterized by decentralization, immutability, anonymity, openness, and self-governance (Nakamoto, 2008), blockchain technology primarily addresses the safety and fairness issues of transactions in trustless environments (Pasdar, Lee & Dong, 2023). A smart contract, defined by Szabo (1997) as a computer protocol designed to propagate, validate, or execute contracts, enables traceable and irreversible transactions without the need for a trusted third party. These contracts are marked by transparency, trustworthiness, automatic execution, and enforcement, and are used to ensure the automatic and faithful execution of transactions in trustless environments (Xiong & Xiong, 2021). The integration of blockchain and smart contracts offers core advantages for data trading, including decentralization, anonymity, and non-repudiation (Puthal et al., 2018; Egunjobi et al., 2024). For instance, public key hash addresses provide anonymity for trading parties (Allouche et al., 2021), while dynamic ownership tracking technology strengthens the rights of data owners (Fadler & Legner, 2022). On-chain encrypted storage combined with real-time verification mechanisms effectively safeguards privacy and ensures transaction non-repudiation (Cai et al., 2024; Oh et al., 2020). In theory, this model promises to systematically resolve issues like lack of fairness, privacy breaches, and single points of failure prevalent in traditional centralized platforms. However, the actual effectiveness of this approach still requires rigorous verification through comprehensive research.

This article aims to clarify the research progress and technical path of decentralized data trading models supported by blockchain technology, providing a theoretical basis for solving the structural defects of the existing trading system. The structure of this article is as follows: “Literature Review” reviews the research status quo of data trading models and blockchain applications; “Methodology” expounds the research methodology; “Exploratory Analysis of Results” presents the exploratory analysis results; “Limitations and Discussions” discusses the theoretical and practical significance of the research findings and explores the limitations and trade-offs of blockchain in data trading applications; “Conclusions” concludes the whole article and looks forward to future research directions.

Literature review

As a core mechanism for the circulation of production factors, data trading has formed a multi-dimensional research system. Early review studies mainly focused on traditional transaction frameworks: Liang et al. (2018) deconstructed price-formation mechanisms, trading-process designs, and data-protection strategies, yet their analysis was confined to centralized market paradigms, failing to predict the disruptive impact of decentralized technologies on trading structures. Xiong & Tang (2021) combed through data-entitlement and pricing research from economic, legal, and computer-science perspectives. Although they revealed the complexity of entitlement definition, they didn’t explore technology-enabled dynamic entitlement paths in depth. Literature on pricing strategies (Cai et al., 2021; Zhang, Jiang & Yuan, 2021; Zhang, Beltrán & Liu, 2023) built theoretical frameworks of game-theory and auction models, but generally lacked technical solutions to the “multiple selling of one item” risk caused by data replicability. Subsequent studies (Fu & Wang, 2022; Wang, Xia & Pei, 2023) expanded to market-operation mechanisms and data-factor connotations but were still limited by the centralized-platform assumption, finding it hard to break the centralized data-sovereignty dilemma.

In recent years, the integrated application of blockchain technology has offered a new paradigm to overcome the aforementioned limitations. Early studies (Guan, Shao & Wan, 2018; Yang et al., 2020) have confirmed the fundamental value of its decentralized, traceable, and tamper-proof features in data storage and transaction auditing. The latest advances show three major breakthroughs:

1. Dynamic entitlement and dispute resolution: automatically execute ownership transfer via smart contracts (Ali, Norman & Azzuhri, 2023; Liu et al., 2024), verify ownership using zero-knowledge proof (Zhou et al., 2023; Xiong et al., 2024; Li et al., 2024a), and effectively curb the risk of data asset loss (Driessen, Monsieur & Van Den Heuvel, 2022; Chen et al., 2024a; Feng et al., 2024).

2. Full-dataset availability guarantee: based on sharding technology and cross-chain protocols, achieve integrity verification and secure aggregation of fragmented data (Song et al., 2023; Abla et al., 2024), and break through the partial data supply bottleneck of traditional models (Wang, 2017; Chen et al., 2024b; Li et al., 2025).

3. Non-repudiable transaction architecture: using Merkle trees and timestamped chain storage, build a real-time verification mechanism for multiple parties (Fernandez, Subramaniam & Franklin, 2020; Fang et al., 2024; Deng, Zuo & Li, 2025), and significantly enhance transaction transparency and traceability.

However, existing research still has significant limitations. First, most blockchain solutions rely on high-computational-power consensus mechanisms, making them ill-suited for high-frequency real-time transactions (Jiang et al., 2023). Second, there is no universal framework for balancing privacy protection and data availability, as performance bottlenecks of technologies like homomorphic encryption hinder practical deployment (Yang et al., 2024). Third, cross-jurisdictional data compliance remains unaddressed (Cai et al., 2024). These gaps indicate that data trading-model innovation needs to integrate technological breakthroughs and institutional design, not just rely on technical tools.

Methodology

This study aims to systematically analyze the current applications of blockchain technology in data trading, with a focus on evaluating its role in ensuring security and fairness throughout transaction processes. By synthesizing high-quality, peer-reviewed literature (2018–2024), we seek to address key challenges and provide actionable insights for improving trust in decentralized data trading. We employed a rigorous three-stage process combining randomized controlled process, blockchain-specific keyword filtering, and quality-assessment tools to ensure methodological robustness. The analysis was guided by the PRISMA 2020 framework and conducted using Web of Science’s Core Collection database (accessed October 2024) to maintain academic rigor.

Data collection process

1. Database selection: Web of Science Core Collection was chosen for its curation of high-impact journals, reducing noise from low-quality sources.

2. Initial search: a title-based search using keywords “data trading” and “blockchain” yielded 164 articles.

3. Filtering workflow:

   • (1)

     Stage 1: removed duplicates and non-peer-reviewed articles (n = 0 for both).

   • (2)

     Stage 2: excluded irrelevant topics (n = 71) via keyword analysis, including 69 articles deemed outside the scope of decentralized security applications after title/abstract screening, and two unretrieved reports.

   • (3)

     Stage 3: screening the remaining 93 articles via AMSTAR 2 retained only those meeting all seven critical domains (Shea et al., 2017), reducing the pool to 18 high-quality studies (see Fig. 1 for the PRISMA flowchart).

Exploratory analysis of results

Systematic literature review, as proposed by Xiao & Watson (2019), is a research methodology designed for the exploratory analysis of previously reported results. This approach is commonly used to assess researchers’ interest in specific topics (Snyder, 2019). It involves collecting and reanalyzing existing research results from both primary and secondary sources.

Table 1 summarises the studies further analysed in this review, listing the authors and main elements of each. Based on this, we classified the reviewed studies into several groups: research on secure data trading as ‘Security’, on fair data trading as ‘Fairness’, and on both secure and fair data trading as ‘Security & Fairness’.

The studies summarized in Table 1 indicate an increasing focus on applying blockchain technology to data trading, particularly regarding security and fairness. Among these studies, eight center on security (Chen et al., 2019b; Camilo et al., 2020; Dai et al., 2019; An et al., 2022; Liu et al., 2022; Tian & Xiao, 2022; Zhao et al., 2023; Xu et al., 2024), while four belong to the fairness category (Li et al., 2020b; Li, Li & Wang, 2022; Li et al., 2024b; Abla et al., 2024). Additionally, six studies (Guan, Shao & Wan, 2018; Li et al., 2020a; An et al., 2021; Park, Jeon & Shin, 2023; Xue et al., 2023; Xiong et al., 2024) were included in the analysis as they address both security and fairness. Thus, this study may be useful for the current research.

The application of blockchain to data trading is driven by two main factors. On one hand, blockchain is proven to enhance the security of data trading. For example, Dai et al. (2019) highlighted the significance of blockchain data processing as a service model. This model can effectively complement the traditional data hosting/swapping as a service model, thus alleviating many security limitations of traditional data trading platforms caused by dishonest buyers or data brokers. On the other hand, blockchain can also ensure fairness in data trading. For example, Li et al. (2020b) proposed a blockchain method for conducting data trading without the need for a centralized trusted third party. This method avoids the risk of data sellers sending data to buyers before receiving payment, which could lead to buyers obtaining data without payment. It also prevents data buyers from failing to receive data after payment.

The following section reviews the methodologies used in previous research on blockchain-based data trading. In addition, it examines the units of analysis that have been the focus of previous studies, as well as the scientific research areas that analyze and identify secure and fair data trading. The implications for managers and researchers are also analyzed.

Limitations and discussions

From an overall perspective, most existing representative studies have focused on leveraging blockchain technology to address the critical issues of security and fairness in data trading. Blockchain’s decentralization and immutability enhance transaction security and data authenticity, while fostering fairer trading processes compared to traditional centralized systems, which often face challenges such as manipulation and data tampering that compromise fairness. However, despite these advantages, existing research has paid insufficient attention to the efficiency challenges arising from blockchain technology use.

These efficiency limitations are actually a direct consequence of the fundamental principles of decentralization and fairness. Decentralization, which lies at the heart of blockchain systems, necessitates consensus mechanisms such as Proof of Work (PoW) or Proof of Stake (PoS) to validate transactions across a vast number of decentralized nodes. While these mechanisms are key to ensuring security and fairness, for example, Bitcoin’s PoW algorithm, which effectively prevents malicious entities from manipulating transactions, inevitably leads to a substantial loss in efficiency. The fact that Bitcoin can process only seven transactions per second, and Ethereum just a few dozen transactions per second, clearly shows that building decentralized consensus naturally restricts scalability. Thus, when striving for decentralization and fairness, compromises in transaction-processing efficiency are often required, and this is a major flaw that existing research hasn’t fully and effectively addressed.

Efficiency is crucial for blockchain technology’s application across economic and social domains. Bitcoin enhances efficiency through sidechains and the Lightning Network, though these solutions compromise fairness by centralizing small transactions. Ethereum seeks improvement via the Proof-of-Stake (PoS) algorithm and sharding technology, yet sharding reduces decentralization by dividing nodes. EOS achieves over 2,000 transactions per second (TPS) using the Delegated Proof-of-Stake (DPoS) mechanism with 21 super nodes, trading decentralization for efficiency.

Security is paramount in blockchain applications, especially given their association with digital currencies and assets. Despite innovations, security concerns persist, as vulnerabilities can lead to irreparable losses. All blockchain systems, from decentralized Bitcoin to multi-centered EOS, must address security challenges.

To effectively apply blockchain technology to data trading, three key issues require urgent attention:

• (1)

  Balancing efficiency and security: blockchain-based data trading systems need both efficiency and security, yet achieving both simultaneously is difficult. Bitcoin’s PoW offers high, mathematically proven security but only 7 TPS. Lowering PoW difficulty to boost consensus speed undermines system security. Existing solutions replacing PoW with PoS, DPoS, or Proof of Authority (PoA) lack proven security.

• (2)

  Balancing transparency and privacy: in blockchain-based data trading, transparency is valued for fair transactions, but it conflicts with privacy. Consortium chains, which balance transparency and privacy, are one solution, yet public chain scenarios still struggle to achieve this balance.

• (3)

  Balancing high concurrency and high throughput: data trading systems require high concurrency to handle numerous simultaneous users and trading requests. However, current blockchain solutions fall short in transaction throughput, failing to meet system requirements. Traditional blockchains use synchronous consensus, verifying transactions before adding them to the chain to synchronize node states. Asynchronous consensus solutions like Tangle, IOTA, and XDAG (using a directed acyclic graph structure) aim to balance concurrency and throughput by first attaching then verifying transactions, though their effectiveness remains unproven.

Conclusions

This systematic review synthesizes the evolving landscape of blockchain applications in data trading, yielding three pivotal insights.

First, blockchain has emerged as a transformative framework for ensuring secure and fair data trading, gaining significant interdisciplinary traction in computer science and engineering. Recent studies (Liu et al., 2022; An et al., 2021; Xiong et al., 2024) show its growing use in addressing vulnerabilities in conventional platforms. It does this particularly through decentralizing trust and automating compliance via smart contracts. This trend aligns with the need to reconcile user reliance on imperfect trading systems (Andrus et al., 2021; Tian et al., 2024) with robust technological safeguards.

Second, methodological analysis reveals a strong focus on computer science-driven innovations. Of the 18 studies reviewed, 83% center on schemes (nine studies) and systems (six studies), emphasizing algorithmic improvements and architectural redesigns over theoretical modeling (three studies). This dominance underscores the field’s applied nature, where solutions like zero-knowledge proofs (Xue et al., 2023; Abla et al., 2024) and hybrid consensus mechanisms (Xu et al., 2024) are tested to balance security and efficiency.

Third, our findings pinpoint three critical dimensions for advancing blockchain-based platforms:

• (1)

  Efficiency-security equilibrium: requires lightweight cryptography to cut computational overhead.

• (2)

  Transparency-privacy duality: needs adaptive frameworks, such as blockchains integrated with federated learning.

• (3)

  Scalability: demands sharding or layer-2 protocols to boost throughput without sacrificing decentralization.

While this review uses the Web of Science corpus, expanding to domain-specific databases (e.g., IEEE Xplore, arXiv) could reduce potential publication bias. Future work should focus on two areas:

• (1)

  Quantitative impact assessments: use metrics like transaction finality time or attack resistance rates to benchmark blockchain against traditional systems.

• (2)

  Multi-objective optimization: identify trade-offs among fairness, security, and efficiency using models like game theory or reinforcement learning. Also, legal-technical interdisciplinary studies are crucial to establish user recourse mechanisms in adversarial situations.

In conclusion, blockchain’s potential to recalibrate data trading ecosystems depends on moving beyond technical proofs-of-concept to holistic, regulation-aware architectures. This shift requires joint efforts to balance cryptographic rigor with usability, ensuring platforms resist exploitation and foster sustainable trust among stakeholders.

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