ABSTRACT
Today, ride-hailing platform operations are popular. Facing pandemics (e.g., COVID-19) some customers feel unsafe for the ride-hailing service and possess a "safety risk-averse" (SRA) attitude. The proportion of this type of SRA customers is unfortunately unknown, which makes it difficult for the ride-hailing platform to decide its optimal service price. In this article, understanding that blockchain technology (BT) based systems can help improve market estimation for the proportion of SRA customers, we conduct a theoretical study to explore the impacts that the BT-based system can bring to the platform, customers, and drivers. We consider the case in which the platform is risk-averse (in profit) and serves a market with both SRA and non-SRA customers. We analytically prove that using BT, the optimal service price will be increased and BT is especially helpful for the case with a more risk-averse ride-hailing platform. However, whether it is more or less significant for the more risk-averse SRA customers depends on their degree of risk aversion. We uncover that when the use of BT is beneficial to the customers, it will also be beneficial to the drivers, and vice versa. We derive in closed-form the analytical conditions under which the use of BT can be beneficial to the ride-hailing platform, customers, and drivers (i.e., achieving "all-win"). When all-win cannot be achieved automatically, we explore how governments can provide sponsors to help. We further extend the analysis to consider the general case in which BT incurs both a fixed cost as well as a cost increasing in demand. We prove that the main conclusion remains robust. In addition, we reveal that the required amount of government sponsor to achieve all-win is the same between the two different costing models explored in this article.
ABSTRACT
Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
ABSTRACT
The healthcare industry and the battle against the COVID‐19 pandemic are two areas where blockchain technology might be useful. In this study, blockchain's significance is examined. Blockchain technology and related procedures will be used in future healthcare systems for collecting sensor data, automated patient monitoring, and safe data storage. Because it can store a large amount of data in a dispersed and secure way and provide access whenever and wherever it is needed, this technology greatly simplifies the process of carrying out activities. The advantages of quantum computing, such as the speed with which patients can be found and monitored, may be fully used with the help of quantum blockchain. Quantum blockchain is an additional resource that may be used to safeguard the veracity, integrity, and availability of stored information. Combining quantum computing with blockchain technology may allow faster and more secure medical information processing. In this research, the authors examine the potential uses of blockchain and quantum technology in the healthcare industry. Quantum technologies, blockchain‐based technologies, and other cutting‐edge ICTs (such as ratification intelligence, machine learning, drones, and so on) were investigated and contrasted in this article. [ FROM AUTHOR] Copyright of Security & Privacy is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
The Internet of Drones (IoD) allows drones to collaborate safely while operating in a restricted airspace for numerous applications in Industry 4.0 world. Energy efficiency and sharing sensing data are the main challenges in swarm-drone collaboration for performing complex tasks effectively and efficiently in real-time. Information security of consensus achievement is required for multi-drone collaboration in the presence of Byzantine drones. Byzantine drones may be enough to cause present swarm coordination techniques to collapse, resulting in unpredictable or calamitous results. One or more Byzantine drones may lead to failure in consensus while exploring the environment. Moreover, Blockchain technology is in the early stage for swarm drone collaboration. Therefore, we introduce a novel blockchain-based approach to managing multi-drone collaboration during a swarm operation. Within drone swarms, blockchain technology is utilized as a communication tool to broadcast instructions to the swarm. This paper aims to improve the security of the consensus achievement process of multi-drone collaboration, energy efficiency, and connectivity during the environment's exploration while maintaining consensus achievement effectiveness. Improving the security of consensus achievement among drones will increase the possibility and stability of multi-drone applications by improving connectivity and energy efficiency in the smart world and solving real environmental issues.
ABSTRACT
COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.
ABSTRACT
Northern Ireland (NI) has pervasively been a fragile and often disputed city-regional nation. Despite NI's slim majority in favour of remaining in the EU, de facto Brexit, post-pandemic challenges and the Northern Ireland Protocol (NIP) have revealed a dilemma: people of all political hues have started to question aspects of their own citizenship. Consequently, this article suggests an innovative approach called 'Algorithmic Nations' to better articulate its emerging/complex citizenship regimes for this divided and post-conflict society in which identity borders and devolution may be facilitated through blockchain technology. This article assesses implications of this dilemma for a city-regionalised nation enmeshed within the UK, Ireland and Europe. This article explores digital citizenship in NI by applying 'Algorithmic Nations' framework particularly relating to intertwined (i) cross-bordering, (ii) critical awareness, (iii) digital activism and (iv) post-pandemic realities and concludes with three dilemmas and how 'Algorithmic Nations' framing could better integrate NI's digital citizenship. [ FROM AUTHOR] Copyright of Citizenship Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Faced with the impact of the coronavirus disease (COVID-19) pandemic, governments must protect the well-being of the population. Aside from considerations, such as keeping the virus from spreading and treating patients, the government should also be concerned about the mental health of its citizens during the epidemic. This study aimed to help users who develop depression due to COVID-19 on social media, reduce the cost of counselling, and reduce the need for users to visit the hospital for counselling. This study investigated the opportunities for blockchain technology to provide psychological help to social media users suffering from depression caused by the pandemic. Blockchain-based technology has been used to develop a new model that enables a user autonomy system to allow users to control their own data fully. The model utilizes a delegated proof of stake consensus blockchain to manage depression data to enable low cost and information security while discussing aspects related to trust, privacy, interoperability, and integration with other information communication technologies. The blockchain framework has been proven to provide secure and reliable information management that meets the user requirements for information autonomy. The frame-work can be combined with various other information technologies to extend its functionality further. Author
ABSTRACT
The latest epidemic of COVID‐19 has significantly impacted both human capital and the global economy, contributing to pandemics and severe global crises. Research into the creation and propagation of the disease is desperately needed. The Internet of Things, cloud computing, and artificial intelligence offer modern technology for real‐time processing for multiple applications such as healthcare applications, transport, traffic control, and so on blockchain is an evolving technology that will dramatically boost transaction protection in finance, supply chain, and other transaction networks. A stable and latency‐sensitive Quality of Service framework for COVID‐19 is the need of an hour. The purpose of this paper is to combine Fog computing and Artificial Intelligence with smart health to establish a reliable platform for early‐stage detection of COVID‐19 infection. A new ensemble‐based classifier is proposed to detect COVID‐19 patients. This research offers a blockchain platform to analyse how the unrelated cases of the COVID‐19 virus can be tracked and identified using peer‐to‐peer, time stamping, and the shared storage advantages of blockchain. In addition to growing patient loyalty, this would effectively enhance the consistency, flexibility, productivity, performance, and effectiveness of healthcare services. The idea of blockchain is used to establish security for the whole framework. Different implementations measure the efficiency of the suggested system. The performance of the proposed framework is evaluated in terms of delay, network usages, RAM usages, and energy consumption. On the other hand, the classifier is evaluated in terms of classifier accuracy, recall, precision, kappa static, and root mean square error. The result shows the performance of the proposed framework and classifier is always better than the traditional frameworks and classifiers. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Recently, innovations in the Internet-of-Medical- Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications. IEEE
ABSTRACT
IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE
ABSTRACT
In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymise the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations. Author
ABSTRACT
Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage. [ FROM AUTHOR] Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving a high localization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users' privacy and security.
ABSTRACT
Blockchain is a cutting-edge technology based on a distributed, secure and immutable ledger that facilitates the registration of transactions and the traceability of tangible and intangible assets without requiring central governance. The agreements between the nodes participating in a blockchain network are defined through smart contracts. However, the compilation, deployment, interaction and monitoring of these smart contracts is a barrier compromising the accessibility of blockchains by non-expert developers. To address this challenge, in this paper, we propose a low-code approach, called EDALoCo, that facilitates the development of event-driven applications for smart contract management. These applications make blockchain more accessible for software developers who are non-experts in this technology as these can be modeled through graphical flows, which specify the communications between data producers, data processors and data consumers. Specifically, we have enhanced the open-source Node-RED low-code platform with blockchain technology, giving support for the creation of user-friendly and lightweight event-driven applications that can compile and deploy smart contracts in a particular blockchain. Additionally, this platform extension allows users to interact with and monitor the smart contracts already deployed in a blockchain network, hiding the implementation details from non-experts in blockchain. This approach was successfully applied to a case study of COVID-19 vaccines to monitor and obtain the temperatures to which these vaccines are continuously exposed, to process them and then to store them in a blockchain network with the aim of making them immutable and traceable to any user. As a conclusion, our approach enables the integration of blockchain with the low-code paradigm, simplifying the development of lightweight event-driven applications for smart contract management. The approach comprises a novel open-source solution that makes data security, immutability and traceability more accessible to software developers who are non-blockchain experts. • EDALoCo, an approach for integrating blockchain and low-code paradigms. • Developing event-driven applications for smart contract management. • Deploying the event-driven applications in lightweight devices. • Providing an open-source solution. [ FROM AUTHOR]
ABSTRACT
Blockchain technology (BCT) as a disruptive innovation has chains (SCs) to effectively deal with severe disruptions, such as those accompanied by COVID-19. To this end, this study explores BCT's role in minimizing the negative impact of such SC disruptions and improving SC resilience. Our study employs semistructured interviews interwoven with thematic analysis to identify the capabilities deployable by BCT at each stage of disruption. Our study reveals key issues associated with contemporary SC networks and the capabilities that can be enhanced by blockchain-enabled SCs to mitigate such issues. Our study further proposes a conceptual framework highlighting the relationships among various phases of disruption, blockchain capabilities, and SC resilience capabilities through the theoretical lens of the dynamic capabilities view. The proposed framework underscores that for a firm operating in a dynamic and rapidly changing environment, BCT can enhance the ability to sense, ability to seize, and ability to maintain. IEEE
ABSTRACT
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organizations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multistakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalization and accuracy.
ABSTRACT
During any emergency, a donation is considered a moral responsibility all over the globe. The lack of transparency and oversight in charity donations hurts people’s enthusiasm to donate. Donors are distrustful about how their funds are utilized. The use of blockchain technology (BCT) will provide a solution to make the donation procedure more viable. It is a distributed technology that offers a secure and transparent environment by avoiding the involvement of third parties between contributors and charities. This article proposed a blockchain-based donation mechanism for the convenience of charity organizations, donors, and beneficiaries during disasters, pandemics such as Covid-19, and other emergencies. All transactions can be traced in blockchain, giving donors visibility into where and how their funds are utilized. This article contributes to improving donations’openness to strengthen public interest in donations and encourage BCT in charity. Ethereum blockchain is used to implement the proposed framework and provides a convenient donation platform. Smart contracts are used to make donations, which build trust between contributors, beneficiaries, and charity organizations. The blockchain-based donation method saves time, lowers donation costs, and eliminates the chances of dubious campaign funds. This study will contribute to improving emergency recovery efforts. IEEE
ABSTRACT
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users’privacy and security. IEEE
ABSTRACT
Under the COVID-19 pandemic, governments worldwide have subsidized manufacturers or consumers on the production or purchase of masks. However, the impacts of these subsidies on the mask supply chain (MSC) operations are unclear. Motivated by our interview with a mask manufacturer as well as the observed real-world practices, we establish consumer utility-based stylized models to analytically examine government subsidies and policies in the MSC. We utilize the infection transmission model to capture the social health risk during the COVID-19 outbreak. The government aims to maximize social welfare, which includes the manufacturer's profit, consumer surplus, social health risk, and government subsidy expenditure. Results indicate that when the price is not controlled (i.e., the manufacturer decides it), the manufacturer and consumer subsidy programs are equally efficient in enhancing consumer surplus as well as reducing harm to social health risk under COVID-19. Thus, the government can conduct a subsidy scheme that is easier to implement in practice. However, we surprisingly find that the government's excessive intervention will cause disequilibrium in the MSC. When the price or the manufacturer's dishonest behavior is fully controlled by the government, subsidizing the MSC is not always advisable. Besides, our findings are consistent with the public interest theory;that is, the proper implementation of dishonesty prevention and pricing control policies can improve social welfare but sacrifice consumer surplus. Our results contribute to healthcare operations management and generate managerial insights for MSC management during COVID-19 with industrial validation.
ABSTRACT
Throughout the various containment phases of a pandemic, such as Covid-19, digital tools and services have proven to be essential measures to counteract the ensuing disrupting effects in social and working interactions. In such scenarios, Nausica@DApp, the comprehensive solution proposed in this paper, eases compatibility of the in-presence activities of a campus-based corporation with the organizational constraints posed by the virus during the pandemic, or at a later endemic stage. This is accomplished throughout several intervention areas, such as personnel contact tracing, crowd gathering surveillance, and epidemiological monitoring. These operational requirements, in particular indirect contact tracing and overcrowd monitoring, call for the adoption of an absolute device localization paradigm, which, in the proposed solution, has been devised on top of the campus WiFi infrastructure, proving to be encouragingly accurate in most cases. Absolute localization, on the other hand, entails a certain amount of server-based centralized operations, which might affect the preservation of user data privacy. The novelty of the proposed solution consists in maximizing confidentiality and integrity in the handling of sensitive personal information, in spite of the centralized aspects of the localization system. This is accomplished by decentralizing contact tracing matching operations, which are entirely carried out locally, by apps running on the users' mobile devices. Contact data are pseudonymized and their authenticity is guaranteed by a blockchain. Furthermore, the proposed novel solution improves privacy preservation by eschewing recourse to the Bluetooth app-to-app channel for user data exchange, in fact a typical choice of most current contract tracing solutions. Thanks to a sensible use of the blockchain features, integrated into Nausica@DApp's microservice-based back-end, a higher degree of operation transparency can be relied upon, thus boosting the user's level of trust and enhancing the availability and reliability of data about people gathering within the campus premises. Moreover, contact tracing only requires the mobile device WiFi interface to be on, so that users are neither forced to adopt new habits, nor to grant additional device access permissions to contact tracing apps (potentially undermining their own privacy). The overall system has been analysed in terms of performance and costs, and the experiments have shown that its adoption is viable and effective.