The integration of blockchain technology into education systems has emerged as a promising approach to enhance transparency, security, and efficiency in academic governance and credentialing workflows. Blockchain's decentralized and immutable nature provides a robust foundation for managing educational records, credentials, and certifications in a tamper-resistant and auditable manner (15). This design enables secure storage and reliable verification of academic achievements, reducing the risk of fraud and misrepresentation and improving trust among educational stakeholders (16). In vocational higher education, blockchain-supported credentialing can facilitate competency-based certification and cross-institution recognition, allowing learners to present verifiable skills and qualifications to potential employers (17). In addition, smart contracts can automate key administrative and governance procedures (e.g., enrollment approval, curriculum updates, and compliance checks), thereby reducing manual overhead and enabling institutions to focus on learning quality and stakeholder coordination. Beyond credentialing, blockchain offers a governance layer for multi-stakeholder collaboration by providing a shared ledger for policy execution, provenance tracking, and accountable data exchange. Such capabilities are particularly relevant in the digital public health context, where collaboration frequently spans institutions and involves sensitive data, heterogeneous requirements, and high-stakes outcomes. By enabling transparent protocol execution and verifiable records, blockchain can support a unified educational framework that aligns curricula with industry needs and provides traceability for collaborative processes and outcomes (18). Moreover, the availability of an auditable transaction history can improve dispute resolution and accountability, which are essential for sustaining long-term collaborations in vocational education ecosystems. The potential of blockchain in education also extends to secure personalization and lifelong learning under strong governance constraints. By enabling permissioned and privacy-aware sharing of student information, blockchain can support data-driven learning analytics while preserving confidentiality and compliance (19). At the same time, it can maintain a persistent record of learning trajectories, certifications, and skill development, facilitating continuous upskilling and career mobility. These studies collectively motivate blockchain-based education systems as an institutional infrastructure for trustworthy governance, credentialing, and cross-sector collaboration, which directly informs the design objectives of our framework. While blockchain provides strong auditability and trust guarantees, it is well recognized that directly storing large and complex educational or public health datasets on-chain can lead to scalability bottlenecks in storage and throughput. Prior work therefore proposes hybrid architectures that keep raw data and heavy artifacts off-chain, while recording only cryptographic commitments, pointers, and verifiable logs on-chain to preserve integrity and provenance (20). Such designs include off-chain storage with on-chain hash anchoring, layer-2 or sidechain mechanisms, and consortium/permissioned blockchains with more efficient consensus for higher throughput. These techniques enable blockchain-based systems to remain scalable while maintaining transparency and accountability, and they are especially relevant for education and healthcare scenarios involving sensitive and high-volume data.

Industry-academia partnerships are essential for bridging the gap between theoretical learning and practical application, particularly in vocational higher education where employability and skill alignment are central goals (21). Prior work emphasizes that effective partnerships require not only curriculum co-design but also mechanisms for coordination, accountability, and sustainable participation, so that education programs remain aligned with evolving industry requirements (22). In digital public health, these partnerships are further challenged by rapidly changing technologies, interdisciplinary collaboration needs, and the involvement of sensitive data, making governance and trust especially important. Blockchain technology has been explored as a means to strengthen industry-academia partnerships by providing a transparent and secure platform for collaboration and resource sharing (23). It can facilitate the management of joint projects and the sharing of research artifacts, datasets, and intellectual property, thereby reducing frictions and promoting trust among stakeholders (24). The decentralized ledger provides a unified record of agreements, contributions, and outcomes, which supports fairness and accountability in collaborative processes (25). These characteristics are aligned with the need for verifiable governance in industry-education collaboration platforms, where disputes over contribution, ownership, and compliance can otherwise undermine long-term cooperation. Furthermore, blockchain-enabled partnerships can support the rapid development of new curricula and training programs that respond to emerging trends in digital public health (26). By incorporating industry feedback and validated skill requirements into transparent coordination workflows, educational institutions can build programs that better match real-world needs, while students benefit from internships, apprenticeships, and applied projects that are formally recorded and verifiable. This line of work motivates our focus on designing programmable governance and incentive mechanisms to support sustainable industry-academia-research collaboration. Such “industry insights” are often generated through data-driven methods and analytics, for example, mining competency requirements from job postings and industry reports, forecasting workforce demand, and analyzing learning outcomes and skill gaps from digital education platforms. These analytics-driven signals can provide quantitative evidence for curriculum design and continuous updating, while blockchain offers a trusted governance and provenance layer to support secure sharing, verifiable attribution, and auditable use of the underlying data and derived insights across stakeholders.

Research collaboration in public health is vital for addressing complex health challenges and improving population health outcomes, as it requires coordinated contributions from multiple institutions and disciplines (27). Such collaborations often involve sensitive data and heterogeneous stakeholders, raising challenges in privacy protection, accountability, provenance, and policy compliance. Blockchain technology offers a promising approach for improving research collaboration by providing an auditable, tamper-resistant infrastructure for data-sharing governance and traceable coordination. Blockchain's decentralized architecture can promote transparency and trust by maintaining a shared ledger of research activities and agreements. In particular, blockchain can support privacy-aware collaboration by enabling controlled access, provenance tracking, and immutable logging of data usage, which helps mitigate concerns about unauthorized access and improves accountability for handling sensitive health information. These properties are especially important in digital public health settings, where multi-party data sharing must satisfy ethical and regulatory constraints while ensuring reproducibility and integrity of scientific outputs. The use of smart contracts can further streamline collaboration workflows by automating key administrative and governance procedures. For example, smart contracts can facilitate grant management and funding allocation by automating proposal submission, review, and approval processes, thereby reducing manual overhead and improving process transparency. Beyond efficiency, such automation can also enforce collaboration rules and reduce opportunistic behavior by ensuring that agreements are executed consistently as defined. These studies highlight blockchain as a practical foundation for accountable public health research collaboration, motivating our framework that integrates governance, privacy-preserving information exchange, and incentive mechanisms to support sustainable industry-academia-research cooperation in digital public health contexts (28). We note that with the emergence of quantum computing, there is an active discussion on the long-term security of blockchain systems that rely on classical public-key cryptography for signatures and identity management (29). This concern is particularly important for sensitive healthcare applications. Our framework mitigates this risk at the data layer by adopting a hybrid on-chain/off-chain design: sensitive medical records remain off-chain within institutional boundaries, and only non-identifiable metadata and cryptographic commitments (hashes) are anchored on-chain (30). In addition, the permissioned consortium setting allows the cryptographic suite to be upgraded as post-quantum cryptography (PQC) standards mature, providing a practical pathway toward quantum-resistant deployment in future iterations.

In this section, we provide a comprehensive overview of our proposed Blockchain-driven Vocational Higher Education Framework designed to enhance Industry-Academia-Research collaboration within the context of Digital Public Health. This framework aims to address the growing need for a more integrated and efficient approach to vocational education that aligns with the rapid advancements in digital health technologies and the increasing demand for skilled professionals in this sector.

The subsequent sections of this paper are structured to systematically introduce and elaborate on the key components and innovations of our framework. In Section 3.2, we lay the groundwork by formalizing the problem space and establishing the necessary theoretical foundations. This includes defining the core concepts and terminologies pertinent to blockchain technology, vocational education, and the digital public health landscape. We also discuss the current challenges and limitations faced by traditional educational models in adapting to the dynamic requirements of the digital health industry.

Following the preliminaries, Section 3.3 introduces our novel educational model, which we have termed the Blockchain-Enhanced Collaborative Learning Model (BECLM). This model leverages the decentralized and secure nature of blockchain technology to create a more transparent, efficient, and adaptable educational ecosystem. We detail the architecture of BECLM, highlighting how it facilitates seamless collaboration between industry partners, academic institutions, and research entities. The model is designed to ensure that educational content remains relevant and up-to-date with industry standards, thereby enhancing the employability of graduates.

In Section 3.4, we present our innovative strategy, named the Blockchain-Enhanced Collaborative Framework (BECF), which outlines the mechanisms through which our model fosters effective knowledge transfer and collaboration among stakeholders. BECF emphasizes the importance of real-time data sharing, collaborative research initiatives, and the co-creation of educational content. We explore how blockchain technology underpins these processes by providing a secure and immutable platform for data exchange, thus building trust and accountability among participants.

Throughout this paper, we aim to demonstrate how our framework not only addresses the existing gaps in vocational education but also sets a new standard for industry-academia-research collaboration in the digital public health domain. By integrating blockchain technology into the educational process, we propose a paradigm shift that aligns educational outcomes with the evolving needs of the digital health industry, ultimately contributing to the development of a more skilled and adaptable workforce.

In this section, we formalize the problem of developing a blockchain-driven vocational higher education framework for industry-academia-research collaboration within the digital public health context. The primary objective is to create a robust and efficient system that leverages blockchain technology to enhance collaboration and data sharing among various stakeholders in the public health sector. This involves defining the key components, interactions, and constraints of the system using mathematical and symbolic representations.

Let E represent the set of educational institutions, I the set of industry partners, and R the set of research organizations. The collaboration network can be modeled as a graph G=(V,L), where V=E∪I∪R is the set of vertices representing all stakeholders, and L is the set of edges representing collaborative relationships.

Each stakeholder v∈V is associated with a set of attributes A_v = {a₁, a₂, …, a_n}, where each attribute a_i represents a specific characteristic or capability, such as expertise in a particular domain, available resources, or technological infrastructure.

The blockchain network is denoted by B=(N,T), where N is the set of nodes, each corresponding to a stakeholder in V, and T is the set of transactions. Each transaction t∈T is a tuple t = (s, r, d, σ), where s is the sender node, r is the receiver node, d is the data or resource being transferred, and σ is the digital signature ensuring the authenticity and integrity of the transaction.

The data integrity and security are maintained through cryptographic hash functions H:{0, 1}^* → {0, 1}^m, where m is the length of the hash output. Each block in the blockchain contains a hash of the previous block, H(b_i−1), ensuring a tamper-proof chain of records.

The consensus mechanism, denoted by C, is a critical component of the blockchain, ensuring that all nodes agree on the state of the blockchain. We consider a proof-of-stake (PoS) model, where the probability of a node being selected to validate a new block is proportional to its stake in the network. Let S_n be the stake of node n∈N, and the probability P(n) of node n being selected is given by:

The educational framework is designed to facilitate the exchange of knowledge and resources among stakeholders. Let K be the set of knowledge units, and Rk the set of resources required for knowledge unit k∈K. In practice, resource allocation is an important aspect of industry-academia-research collaboration (e.g., training budgets, internship opportunities, and shared facilities). While such allocation can be formulated as a constrained optimization problem, the primary focus of this paper is the blockchain-enabled governance and incentive mechanisms (BECLM/BECF) for trustworthy collaboration and privacy-aware information exchange. We therefore leave detailed optimization-based resource allocation and large-scale system simulation as future work.

Choosing a consensus mechanism is critical for balancing security, scalability, and governance requirements. In our setting, the collaboration network is a permissioned consortium involving authenticated stakeholders (universities, research institutions, and industry partners), where identities are known and participation is governed by access control. As a result, PoW is not suitable due to its high energy cost and low throughput, while classical Byzantine protocols (e.g., PBFT-style consensus) can suffer from communication overhead that grows quickly with the number of participants. We therefore adopt a modified PoS mechanism to achieve efficient validation with low latency, while enabling incentive-aligned governance by coupling stake with verified contribution and reputation. We acknowledge that PoS may face limitations such as potential centralization (“rich-get-richer”) and scalability bottlenecks if validator selection is dominated by a small subset of nodes. To mitigate these issues, our modified PoS incorporates (i) stake as a composite score that reflects both long-term verified contribution and compliance history (rather than capital-only staking), (ii) reputation decay and stake capping to prevent persistent dominance, (iii) periodic validator rotation and committee-based validation to improve decentralization and robustness, and (iv) a hybrid on-chain/off-chain design where only lightweight metadata and commitments are recorded on-chain to reduce throughput pressure. These design choices make PoS practical and better aligned with the governance goals of industry–academia–research collaboration in digital public health. In particular, PBFT-style protocols require all-to-all communication among validators, which can become a bottleneck as the number of consortium members grows, whereas PoS-based committee validation allows scalable validator selection with bounded per-round communication.

As illustrated in Figure 2, our framework integrates stakeholder collaboration, governance, and privacy-aware information exchange into a unified blockchain-driven architecture.

In this section, we introduce the Blockchain-Enhanced Collaborative Learning Model (BECLM), a novel framework designed to facilitate industry-academia-research collaboration within the digital public health context. The BECLM leverages blockchain technology to ensure secure, transparent, and efficient data sharing and collaboration among stakeholders. This model addresses the challenges of data integrity, privacy, and trust, which are critical in the sensitive domain of public health education and research. Figure 3 visualizes the BECLM component, where bidirectional links represent two-way knowledge and data exchange among universities, research institutions, industry partners, and public health stakeholders.

The BECLM is structured around a decentralized network of nodes, each representing a participating entity such as a university, research institution, or industry partner. These nodes are interconnected through a blockchain ledger, which records all transactions and interactions in a tamper-proof manner. The model is designed to support various collaborative activities, including joint curriculum development, research projects, and knowledge exchange.

To formalize the BECLM, we define the following components and their interactions:

Let N = {n₁, n₂, …, n_k} represent the set of nodes in the network, where each n_i corresponds to a participating entity. The blockchain ledger L is a distributed database that maintains a chronological record of all transactions T = {t₁, t₂, …, t_m} among the nodes. Each transaction t_j is a tuple (s, r, d, σ), where s is the sender node, r is the receiver node, d is the data or resource being shared, and σ is the digital signature ensuring authenticity.

The consensus mechanism employed in BECLM is a modified Proof of Stake (PoS) algorithm, which ensures that only authorized nodes can validate transactions. Let S = {s₁, s₂, …, s_l} be the set of stakeholders with validation rights, where each s_i is selected based on their stake in the network, defined as stake(si)=resources contributed bysitotal resources in the network.

The model also incorporates smart contracts, denoted as C = {c₁, c₂, …, c_n}, which automate the execution of predefined agreements between nodes. Each smart contract c_k is a function f:X→Y, where X is the set of input conditions and Y is the set of outcomes. The execution of a smart contract is triggered when the conditions X are met, ensuring that collaborative activities are carried out as agreed.

To enhance data privacy, the BECLM employs cryptographic techniques such as homomorphic encryption and zero-knowledge proofs. Let E:D→D′ be the encryption function, where D is the original data and D′ is the encrypted data. The decryption function E⁻¹:D′ → D ensures that only authorized nodes can access the original data.

The BECLM also supports a reputation system, R:N → ℝ, which assigns a reputation score to each node based on their past interactions and contributions. The reputation score influences the node's ability to participate in future collaborations and validate transactions. From a machine learning perspective, BECLM can be instantiated as a blockchain-based federated learning workflow, in which multiple stakeholders perform decentralized local training and periodically contribute model updates for global aggregation under auditable governance and incentive alignment.

In the rapidly evolving landscape of digital public health, the integration of blockchain technology into vocational higher education presents a unique opportunity to enhance industry-academia-research collaboration. Our proposed strategy, termed the Blockchain-Enhanced Collaborative Framework (BECF), leverages the decentralized and secure nature of blockchain to address the challenges of data sharing, trust, and transparency in collaborative environments. The incentive and constraint mechanism (BECF) is summarized in Figure 4, highlighting how token rewards and contribution-aware reputation regularize participation quality in the collaborative process.

The BECF is designed to facilitate seamless interaction between educational institutions, industry partners, and research entities by establishing a shared, immutable ledger of educational credentials, research outputs, and collaborative agreements. This ledger acts as a single source of truth, ensuring that all stakeholders have access to verified and up-to-date information, thereby reducing the potential for disputes and enhancing trust among parties.

A key component of the BECF is the implementation of smart contracts, which automate the execution of agreements and transactions based on predefined conditions. These smart contracts are particularly useful in managing intellectual property rights, funding disbursements, and performance evaluations, as they ensure that all parties adhere to the agreed terms without the need for intermediaries. The use of smart contracts not only streamlines administrative processes but also reduces the risk of human error and fraud.

To further enhance the collaborative potential of the BECF, we introduce a token-based incentive system that rewards participants for their contributions to the network. Tokens can be earned through activities such as publishing research, developing educational content, or participating in industry projects. These tokens can then be exchanged for access to premium resources, professional development opportunities, or even financial compensation. By aligning incentives with collaborative goals, the BECF encourages active participation and fosters a culture of innovation and continuous improvement.

The BECF also incorporates advanced data analytics capabilities, enabling stakeholders to gain insights into collaboration patterns, educational outcomes, and research impact. By analyzing data stored on the blockchain, institutions can identify areas for improvement, optimize resource allocation, and tailor educational programs to meet the evolving needs of the industry. This data-driven approach ensures that the educational offerings remain relevant and aligned with the demands of the digital public health sector.

To balance transparency and privacy, the framework adopts a hybrid on-chain/off-chain architecture. Raw educational records and sensitive public health data (e.g., learner profiles, institutional datasets, and patient-level EHR) are stored and processed off-chain within each institution's secure boundary. Only lightweight, non-identifiable metadata and cryptographic commitments are written on-chain, including (i) model update hashes and version pointers, (ii) signed transaction logs of collaboration events, (iii) reputation and stake states, and (iv) smart-contract execution traces (e.g., reward issuance and agreement fulfillment). This design ensures auditability and non-repudiation while minimizing exposure of sensitive content.

In BECLM, stake is defined as a composite measure reflecting a participant's long-term contribution and reliability in the collaboration network. Specifically, for node i, we maintain a non-negative stake score S_i that is updated based on (a) historical contribution utility (e.g., validation AUROC improvement induced by its submitted updates), (b) protocol compliance (e.g., timely submission, format correctness, and audit pass rate), and (c) accumulated reputation from prior rounds. We implement S_i as an exponential moving average of these factors to avoid short-term gaming. The PoS validator sampling probability is then P(i)=Si/∑jSj, ensuring that more reliable and consistently beneficial participants are more likely to validate and propose blocks.

BECF implements a contribution-driven incentive mechanism using smart-contract-issued tokens. For each round r, node i receives a reward proportional to its verified contribution score ci(r), which is computed from the validation improvement brought by its update and discounted by potential anomalies. Concretely, we use

where λ controls incentive strength and μ penalizes failed audits or protocol violations. Rewards are capped to prevent extreme dominance and are only issued after the update passes verification by PoS-selected validators. This design encourages informative participation, discourages noisy or adversarial updates, and yields more stable global aggregation under heterogeneous institutional data distributions.

Educational collaboration agreements (e.g., joint curriculum development, industry internship quotas, research deliverables, and resource sharing) are encoded as smart-contract states and conditional triggers. Each agreement specifies participants, obligations, deadlines, evaluation rules, and reward distribution. The contract automatically (i) logs commitments, (ii) verifies fulfillment signals, and (iii) executes reward/penalty actions. Algorithm 1 outlines the core contract logic used in our prototype.

The framework is designed to support common regulatory principles in GDPR, HIPAA, and PIPL, including data minimization, purpose limitation, access control, auditability, and confidentiality. Sensitive records remain off-chain within institutional boundaries, and only cryptographic commitments and non-identifiable metadata are stored on-chain, reducing exposure risk. Access to off-chain artifacts is governed by permissioned policies and logged via immutable on-chain audit trails, supporting accountability and post-hoc compliance checks. Third, privacy-preserving techniques enable collaboration without revealing raw personal data. Finally, smart-contract rules enforce protocol compliance and provide transparent execution traces, which is beneficial for audit and governance in regulated digital public health settings.

We study supervised binary classification for early risk prediction in intensive care units (ICUs). Given a fixed-length multivariate time-series X∈ℝ^T×D extracted from the first 48 h after ICU admission, where clinical variables (e.g., vital signs, laboratory tests, medications, and demographics) are aggregated on an hourly grid (T = 48), the goal is to predict a binary outcome Y∈{0, 1} indicating whether the patient dies during the same hospital stay (in-hospital mortality). Each patient episode is represented as a 48-step sequence (1-h resolution), and the model outputs a probability p^=P(Y=1∣X).

The supervision signal is obtained from the datasets' hospital outcome records: Y = 1 if in-hospital death is recorded for the admission and Y = 0 otherwise. Therefore, labels are derived from system-reported clinical outcomes rather than manual annotation, and training is performed with standard supervised learning using patient-level outcome labels.