Data sharing has become a key technology to break down data silos in the big data era. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in secure data-sharing schemes to realize flexible and fine-grained access control. However, in traditional CP-ABE schemes, the access structure is directly shared along with the ciphertext, potentially leading to users’ private information leakage. Outsourcing data to a centralized third party can easily result in privacy leakage and single-point bottlenecks, and the lack of transparency in data storage and sharing casts doubts whether users’ data are safe. To address these issues, we propose a blockchain-based CP-ABE scheme with partially hidden access structures (BCP-ABE-PHAS) to achieve fine-grained access control while ensuring user privacy. First, we propose an efficient CP-ABE scheme with partially hidden access structures, where the ciphertext size is constant. To assist data decryption, we design a garbled Bloom filter to help users quickly locate the position of wildcards in the access structure. Then, to improve storage efficiency and system scalability, we propose a data storage scheme that combines blockchain technology and the interplanetary file system, ensuring data integrity. Finally, we employ smart contracts for a transparent data storage and sharing process without third-party participation. Security analysis and performance evaluation show that the proposed BCP-ABE-PHAS scheme can preserve policy privacy with efficient storage and low computational overhead.
With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.
Circulated record advances (DLTs) are getting a lot of consideration. As interest grows in the future applications of DLTs, contributions to Blockchain-as-a-Service (BaaS) are increasing to provide a secret supporting base. BaaS entails a specialist organisation providing and managing components of a DLT system in order to promote and enhance efficiencies in the creation, experimentation, arrangement, and progression of DLT applications across the board.
In any case, a significant part of the enthusiasm for DLTs originates from their capability to decentralize, disintermediate, and empower 'trustless' associations. From the start sight, BaaS
– being offered by a supplier – seems to contradict this. Practically speaking, regardless of whether BaaS raises substantive trust concerns relies upon the idea of the contribution, the application's points of interest, and the members' objectives and hazard hunger.
Nowadays, the adoption of Internet of Things (IoT) technology worldwide is accelerating the digital transformation of healthcare industry. In this context, smart healthcare (s-healthcare) solutions are ensuring better and innovative opportunities for healthcare providers to improve patients' care. However, these solutions raise also new challenges in terms of security and privacy due to the diversity of stakeholders, the centralized data management, and the resulting lack of trustworthiness, accountability, and control. In this paper, we propose an end-to-end Blockchain-based and privacy-preserving framework called SmartMedChain for data sharing in s-healthcare environment. The Blockchain is built on Hyperledger Fabric and stores encrypted health data by using the InterPlanetary File System (IPFS), a distributed data storage solution with high resiliency and scalability. Indeed, compared to other propositions and based on the concept of smart contracts, our solution combines both data access control and data usage auditing measures for both Medical IoT data and Electronic Health Records (EHRs) generated by s-healthcare services. In addition, s-healthcare stakeholders can be held accountable by introducing an innovative Privacy Agreement Management scheme that monitors the execution of the service in respect of patient preferences and in accordance with relevant privacy laws. Security analysis and experimental results show that the proposed SmartMedChain is feasible and efficient for s-healthcare environments.
Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the service provider has complete access to the collected traffic data and represent a single point of failure and trust. In this paper, we propose GOLIATH, a blockchain-based decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network's participants. Our approach mitigates the limitations of existing crowd-sourcing centralized solutions by guaranteeing trusted information collection and exchange, fully exploiting the intrinsic distributed nature of vehicles. We demonstrate its feasibility in the context of vehicle positioning and traffic information management. Each vehicle participating in the decentralized network shares its position and neighbors' ones in the form of a transaction recorded on the ledger, which uses a novel consensus mechanism to validate it. We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication. We evaluate the proposed framework in a simulated (but realistic) environment, which considers different threats and allows showing its robustness and safety properties.
Cloud computing provides a feasible solution to data outsourcing, and hence forming a cloud-based data market, where data users buy data from owners through querying cloud servers. However, it also incurs new privacy and security problems, as data is under a centralized third-party instead of the data owner’s direct control. Existing data markets are also questioned on their inflexible and opaque pricing, where the value of data ownership and the cost of query searches are mixed. In this paper, we consider blockchain-based storage as a better choice to ensure safe data outsourcing since data is spread out across many data points. We propose an Ethereum-based data market that provides distributed storage and correct remote data search. We design a new pricing model, where each query will be charged by two parties: owner (paid for providing his data) and miner (rewarded by performing query searches). We study a new cooperative search scheme through a proxy to reduce cost on the user side. Given that each user query is charged based on its number of keywords, then a cooperative search can reduce user-side cost by combining multiple queries into a group so that overlapped keywords will only be charged for one time. To ensure user QoE, a combined query should not be significantly larger than any of its original queries in terms of the number of keywords. The total price is based on the total number of keywords in all groups. Since it is a cooperative model with shared resources, we also study various incentive properties on the user side, yielding a cost sharing mechanism to split joint cost in a truth-revealing and fair manner. We further extend our market with a set of substitute data owners and propose a double auction mechanism to match users and owners based on their requirements. Experiments have been conducted on real query trace to demonstrate the effectiveness of our proposed scheme.
Md. Zıa Ur Rahman, Sala Surekha, Krishna Prasad Satamraju, Shafi Shahsavar Mirza · 5 authors
Healthcare is one of the largest domains across the globe both in terms of employment and income generation. It is continuously evolving and exploiting new technological dimensions to incorporate innovations for providing universal health coverage. Sharing of data collected by various sensors deployed at the patient’s end is a growing solicitude in healthcare as the privacy of the personal healthcare data is of paramount importance. In this paper, a blockchain based healthcare framework is proposed to address the problems related to the sensor-based patient vital body parameters collection, monitoring, secured data storage and sharing among different stakeholders. This framework uses Internet of Medical Things (IoMT) devices interfaced with MAX30205 (human body temperature), and a blood pressure including heart-rate measuring device to collect the patient vital parameters. A Markov state chain is modeled to monitor the patient medical states through various phases during the treatment and monitoring process. Application layer protocols MQTT, CoAP, and AMQP are evaluated for latency and packet loss during data transfers. Ethereum permissioned blockchain is used to deploy the proposed model. Smart contracts provide access control only to authorized users. A comparative analysis is provided in the end to highlight the merits of the proposed model over the existing similar methods. Due to the parallelism and use of blockchain, the system reports 80% improvement in terms of nodes and transaction scalability compared to the existing systems.
Aiming at the problems of large ciphertext size and low efficiency in the current secure multi-party computation (SMC) protocol based on fully homomorphic encryption (FHE), the paper proves that the fully homomorphic encryption scheme that supports multi-bit encryption proposed by Chen Li et al. satisfies the key homomorphism. Based on this scheme and threshold decryption, a three-round, interactive, leveled, secure multi-party computation protocol under the Common Random String (CRS) model is designed. The protocol is proved to be safe under the semi-honest model and the semi-malicious model. From the non-interactive zero-knowledge proof, it can be concluded that the protocol is also safe under the malicious model. Its security can be attributed to the Decisional Learning With Errors (DLWE) and a variant of this problem (some-are-errorless LWE). Compared with the existing secure multi-party computation protocol based on fully homomorphic encryption under the CRS model, the ciphertext size of this protocol is smaller, the efficiency is higher, the storage overhead is smaller, and the overall performance is better than the existing protocol.
BACKGROUND: In clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues. OBJECTIVE: This study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance. METHODS: The ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system. RESULTS: We describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes. CONCLUSIONS: Blockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.
Peng Wang, Ning Xu, Haibin Zhang, Wen Sun · 5 authors
The Internet of Things (IoT), while providing comprehensive interconnection and ubiquitous services, poses security issues by enabling resources sharing among various devices from different untrusted authorities. Blockchain, as a distributed ledger, provides a traceable and verifiable platform to ensure the secure access control in IoT. The existing works based on blockchain may bring up intolerable computing overhead and delay to the lightweight IoT devices. In this article, we propose a dynamic and lightweight attribute-based access control framework for blockchain-empowered IoT, to achieve secure and fine-grained authorization. The proposed scheme allows access to resources by evaluating attributes, operations, and the environment relevant to a request. The access policy is executed through smart contract in blockchain for security and flexibility. To further adapt to IoT device constraints, we design a access control framework based on decentralized application (DApp), which can maintain tamper proof in a timely manner and be adapt to the delay-intolerant application. When delay-intolerant access is required, access can be allowed according to local replica of the blockchain, without a consensus of blockchain network. Considering the time-varying attributes of IoT devices, a trust management scheme is proposed based on the Markov chain to resist the security fluctuation caused by the vulnerability of IoT devices. In the experiments, we deploy our system prototype on Ethereum to evaluate the feasibility and effectiveness of the scheme. The results show the proposed scheme can achieve secure, high throughput, and flexible access control in IoT.
The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption and decryption is usually directly stored and managed by users or third-party organizations, which has security and privacy implications. To address this potential security and privacy risk, we propose a Shamir threshold cryptography scheme for IIoT data protection using blockchain: STCChain. Specifically, in our solution, the edge gateway uses a symmetric key to encrypt the data uploaded by the IoT device and stores it in the cloud. The symmetric key is protected by a private key generated by the edge gateway. To prevent the loss of the private key and privacy leakage, we use a Shamir secret sharing algorithm to divide the private key, encrypt it, and publish it on the blockchain. We implement a prototype of STCChain using Xuperchain, and the results show that STCChain can effectively prevent attackers from stealing data as well as ensuring the security of the encryption key.
Siqi Zeng, Kosuke Kaneko, Yan Ke, Haowen Tan · 5 authors
This paper proposes a design of escrow scheme with blockchain technology. The purpose of this design is to solve the problem in overtaking service [1]. To achieve the objective, we take several simulations to evaluate the performance of our design. The paper will also introduce the reason why introducing the escrow scheme into blockchain technology. And the paper will briefly discuss the advantages and disadvantages of the design.
With the development of location-based services based on smart terminals and positioning technologies, location forgery and privacy leakage become an important issue, making it difficult for the existing methods of proof of location to guarantee the accuracy, authenticity and privacy of user trajectory information. By using the decentralized, non-tamperable and traceable characteristics of blockchain and combining with zero-knowledge proof method, this paper proposes a personal trajectory verification algorithm to provide a solution to solve the limitation of traditional location proof method for trajectory verification problems.
With the proliferation of social networks, user data is increasingly vulnerable to unauthorized access, profiling, and data breaches. Cryptography has emerged as a critical defense mechanism to ensure data confidentiality, integrity, and user privacy. This paper explores the application of modern cryptographic techniques—such as homomorphic encryption, zero-knowledge proofs, and attribute-based encryption—to enhance privacy controls within social networking platforms. It further highlights the challenges of balancing usability with security and presents solutions for deploying cryptographic protocols in dynamic, large-scale environments like Facebook, Twitter, and LinkedIn. The paper concludes with future directions for integrating decentralized privacy models using blockchain-based cryptographic frameworks.
The continuous development of the mobile computing environment has led to the emergence of fintech to enable convenient financial transactions in this environment. Previously proposed financial identity services mostly adopted centralized servers that are prone to single-point-of-failure problems and performance bottlenecks. Blockchain-based self-sovereign identity (SSI), which emerged to address this problem, is a technology that solves centralized problems and allows decentralized identification. However, the verifiable credential (VC), a unit of SSI data transactions, guarantees unlimited right to erasure for self-sovereignty. This does not suit the specificity of the financial transaction network, which requires the restriction of the right to erasure for credit evaluation. This paper proposes a model for VC generation and revocation verification for credit scoring data. The proposed model includes double zero knowledge - succinct non-interactive argument of knowledge (zk-SNARK) proof in the VC generation process between the holder and the issuer. In addition, cross-revocation verification takes place between the holder and the verifier. As a result, the proposed model builds a trust platform among the holder, issuer, and verifier while maintaining the decentralized SSI attributes and focusing on the VC life cycle. The model also improves the way in which credit evaluation data are processed as VCs by granting opt-in and the special right to erasure.
With the wide deployment of Internet of Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this article, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic signal to interference plus noise ratio (SINR) model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach$k$-times consensus among$n$devices within$O(k+\log n)$time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since$\Omega (k+\log n)$is a lower bound to achieve$k$-times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.
James A. Cunningham, Gail Davidge, Nigel Davies, Sarah Devaney · 9 authors
Data providers holding sensitive medical data often need to exchange data pertaining to patients for whom they hold particular data. This involves requesting information from other providers to augment the data they hold. However, revealing the superset of identifiers for which a provider requires information can, in itself, leak sensitive private data. Data linkage services exist to facilitate the exchange of anonymized identifiers between data providers. Reliance on third parties to provide these services still raises issues around the trust, privacy and security of such implementations. The rise and use of blockchain and distributed ledger technologies over the last decade has, alongside innovation and disruption in the financial sphere, also brought to the fore and refined the use of associated privacy-preserving cryptographic protocols and techniques. These techniques are now being adopted and used in fields removed from the original financial use cases. In this paper we present a combination of a blockchain-native auditing and trust-enabling environment alongside a query exchange protocol. This allows the exchange of sets of patient identifiers between data providers in such a way that only identifiers lying in the intersection of sets of identifiers are revealed and shared, allowing further secure and privacy-preserving exchange of medical information to be carried out between the two parties. We present the design and implementation of a system demonstrating the effectiveness of these exchange protocols giving a reference architecture for the implementation of such a system.
The high-speed movement of nodes and the burstiness of interactions in the Internet of Vehicles pose huge challenges to the trusted vehicle collaboration and data sharing. Aiming at the disadvantages of existing authentication mechanisms and trust management models for connected vehicles, this article proposes a trust management model enabled by blockchain to ensure the traceability, nontampering, unforgeability, and transparency of vehicular interactions. The proposed trust management model leverages Dirichlet distribution, reputation regression, and revocation punishment to objectively and accurately reflect the trust status of vehicles. Simulation results on real-world data sets show that the proposed trust management model advantageously improves the accuracy of malicious vehicle detection and the attack resistance of connected vehicles.
The digitization of Electronic Medical Record (EMR) provides potential access to a wealth of medical information, but also presents new challenges in privacy-preserved EMR exchanging and sharing. In this paper, we propose a blockchain-based smart healthcare system with fine-grained privacy protection for reliable data exchanging and sharing among different users. We design a blockchain-enabled dynamic access control framework combined with Local Differential Privacy (LDP) strategies to provide the attribute-based privacy protection in transaction workflow. We design four types of smart contracts in the framework to meet the requirements of anonymous transaction, dynamic access control, beneficial matching decision, and evaluation of published data in an open network. To satisfy fine-grained privacy protection, we classify sensitive attributes of EMRs into different levels and set differential privacy budgets to randomize attributes before data publishing. Also, we design data quality function to depict the disturbance incurred by LDP-based privacy preferences at the requester view, and present appropriate many-to-many matching decisions among participants for beneficial transactions. Finally, we develop a prototype system and test our approach using 200,000 real-world EMRs. Experimental results show that the proposed privacy-preserved scheme can make stable and reliable transactions between EMR publishers and requesters. The prototype system achieves individual-centric privacy configuration at the patient site, while providing error-guaranteed statistics at the requester site. Additionally, the access control policies, logs of anonymous transaction are kept in the blockchain to provide system-level traceability.
Chitra Karunakaran, K Madhura Ganesh, Sonya Ansar, Rohitha Subramani
Electronic Health Records (EHR) is the electronic form of storing a patient's medical history. EHR contains patient’s data such as progress notes, medications, prescriptions, vital signs, scan reports and laboratory data. Transferring EHR over the internet improves the quality of health care and reduces medical costs. However, in the traditional system, the EHR are stored across different decentralised hospitals, making data sharing difficult and increasing the risk of patient privacy. A privacy-preserving framework for electronic health records using blockchain technology is implemented to address these issues. The patient has complete control over the EHR, and the patient can share their health records with doctors of various medical institutions. The privacy and security of the patient’s EHR are guaranteed by the verifiability and immutability property of the blockchain technology. The doctor upload the EHR, and it is encrypted using the SHA256 hashing algorithm and stored as a separate block. The patient shares the EHR with the doctor of any medical institution through the unique key shared via the doctor’s email. The doctor can access and update the EHR using the shared key. The block validation is done using Delegated Proof of Stake (DPoS) consensus algorithm, which guarantees the privacy of the patient’s data. The proposed system based on the DPoS algorithm has considerabe reduction in resource utilisation, computational capacity, time, and cost for EHR transactions.
Many researchers have proposed replacing the aggregation server in federated learning with a blockchain system to improve privacy, robustness, and scalability. In this approach, clients would upload their updated models to the blockchain ledger and use a smart contract to perform model averaging. However, the significant delay and limited computational capabilities of blockchain systems make it inefficient to support machine learning applications on the blockchain. In this paper, we propose a new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. Our architecture inherits the merits of traditional blockchain systems while achieving low latency and low resource consumption by waiving global consensus. To evaluate the performance and robustness of our architecture, we implemented a prototype and tested it on a physical four-node network, and also developed a simulator to simulate larger networks and more complex situations. Our experiments show that the DFL architecture can reach over 90\% accuracy for non-I.I.D. datasets, even in the presence of model poisoning attacks, while ensuring that the blockchain part consumes less than 5\% of hardware resources.