With the popularity of the internet 5G network, the network constructions of hospitals have also rapidly developed. Operations management in the healthcare system is becoming paperless, for example, via a shared electronic medical record (EMR) system. A shared electronic medical record system plays an important role in reducing diagnosis costs and improving diagnostic accuracy. In the traditional electronic medical record system, centralized database storage is typically used. Once there is a problem with the data storage, it could cause data privacy disclosure and security risks. Blockchain is tamper-proof and data traceable. It can ensure the security and correctness of data. Proxy re-encryption technology can ensure the safe sharing and transmission of relatively sensitive data. Based on the above situation, we propose an electronic medical record system based on consortium blockchain and proxy re-encryption to solve the problem of EMR security sharing. Electronic equipment in this process is connected to the blockchain network, and the security of data access is ensured through the automatic execution of blockchain chaincodes; the attribute-based access control method ensures fine-grained access to the data and improves the system security. Compared with the existing electronic medical records based on cloud storage, the system not only realizes the sharing of electronic medical records, but it also has advantages in privacy protection, access control, data security, etc.
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.
Mingyu Liang, Ioanna Karantaidou, Foteini Baldimtsi, Steven Gordon · 5 authors
Abstract We propose a new theoretical approach for building anonymous mixing mechanisms for cryptocurrencies. Rather than requiring a fully uniform permutation during mixing, we relax the requirement, insisting only that neighboring permutations are similarly likely. This is defined formally by borrowing from the definition of differential privacy. This relaxed privacy definition allows us to greatly reduce the amount of interaction and computation in the mixing protocol. Our construction achieves O ( n· polylog( n )) computation time for mixing n addresses, whereas all other mixing schemes require O ( n 2 ) total computation across all parties. Additionally, we support a smooth tolerance of fail-stop adversaries and do not require any trusted setup. We analyze the security of our generic protocol under the UC framework, and under a stand-alone, game-based definition. We finally describe an instantiation using ring signatures and confidential transactions.
Shan Wang, Ming Yang, Tingjian Ge, Yan Luo · 5 authors
Chain of custody is needed to document the sequence of custody of sensitive big data. In this paper, we design a blockchain big-data sharing system (BBS) based on Hyperledger Fabric. We denote the data stored outside of a ledger for sharing as "off-state" and "big data" (referring to extremely large data) is in this category. In our off-state sharing protocol, a sender registers a file with BBS for sharing. To acquire the file, an authenticated and authorized receiver has to use transactions and interacts with BBS in four phases, including the file transfer request, encrypted file transfer, key retrieval, and file decryption. The corresponding transactions are recorded in the ledger and serve as chain of custody to document the trail of the data. Compared with related work, BBS can perform the four phases autonomously. It utilizes the permissioned blockchain, i.e. Hyperledger Fabric, for access control and can defeat dishonest receivers. We design and implement a prototype of BBS for big file sharing. Extensive experiments were performed to validate its feasibility and performance.
5G heterogeneous network (HetNet) is a novel network topology that integrates various kinds of wireless access technologies such as 4G Long-Term Evolution (LTE), Wi-Fi, and so on. Despite greatly improving spectrum efficiency, it poses enormous challenges to spectrum e-auction. Firstly, due to high mobility, bidders may be interested in different spectrums in terms of time or geolocation. Secondly, one’s bidding value should be protected against rival bidders or adversaries to avoid vicious competition as well as privacy leakage. Thirdly, the ubiquitous HetNet requires a trustworthy distributed auction framework rather than a centralized auctioneer-based pattern. Aiming at overcoming these obstacles above, we proposed a blockchain-based combinatorial spectrum e-auction framework. Different from other blockchain-based solutions of using SGX to realize trust processing in the auction phase, we adopt Zether, a privacy-preserving smart contract, as the main building block. Besides, the bidding value is preserved from the beginning to the end, even though the time-consuming Paillier homomorphic encryption and garbled circuits are absent. We provide the auction security by leveraging <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi mathvariant="normal">Σ</a:mi> </a:math> -Bullets, a zero-knowledge proof mechanism. Theoretical analysis and extensive evaluation also indicate that our approach is better than the state-of-the-art works in terms of efficiency and effectiveness.
Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system.
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.
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.
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.
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.
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.
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.
The current medical cyber physical systems involve a wide range of institutions and a large number of participants. Data sharing among distributed medical institutions is already a development trend. However, the security is worrying; e.g., the access to medical data lacks uniformity and standardization. What is more, data is easy to be tampered with and leaked. This has a very negative impact on the medical industry. Therefore, a strict and reliable access control mechanism for data in the medical cyber physical systems is a prerequisite for ensuring the implementation of modern medical functions. We deal with how to design effective access control in medical cyber physical systems. Combined with blockchain technology, we design the medical cyber physical systems based on blockchain data access control mechanism and unite data in the chain of union Fabric network resources access control. We qualitatively classify medical data, define the weight level of different data, design a medical data access framework based on blockchain, build an applicable model, formulate access control strategy, and specify the role assignment and access task matching of users, so as to achieve secure and effective data access control. The Hyperledger Fabric network is established as the alliance chain for managing access control rights distribution through smart contracts so as to achieve case-based medical data access control under the blockchain.
With the integration of the Internet of Things (IoT) in the field of transportation, the Internet of Vehicles (IoV) turned to be a vital method for designing Smart Transportation Systems (STS). STS consist of various interconnected vehicles and transportation infrastructure exposed to cyber intrusion due to the broad usage of software and the initiation of wireless interfaces. This study proposes a federated deep learning-based intrusion detection framework (FED-IDS) to efficiently detect attacks by offloading the learning process from servers to distributed vehicular edge nodes. FED-IDS introduces a context-aware transformer network to learn spatial-temporal representations of vehicular traffic flows necessary for classifying different categories of attacks. Blockchain-managed federated training is presented to enable multiple edge nodes to offer secure, distributed, and reliable training without the need for centralized authority. In the blockchain, miners confirm the distributed local updates from participating vehicles to stop unreliable updates from being deposited on the blockchain. The experiments on two public datasets (i.e., Car-Hacking, TON_IoT) demonstrated the efficiency of FED-IDS against state-of-the-art approaches. It reveals the credibility of securing networks of intelligent transportation systems against cyber-attacks.
Data sharing is very important for accelerating scientific research, business innovations, and for informing individuals. Yet, concerns over data privacy, cost, and lack of secure data-sharing solutions have prevented data owners from sharing data. To overcome these issues, several research works have proposed blockchain-based data-sharing solutions for their ability to add transparency and control to the data-sharing process. Yet, while models for decentralized data sharing exist, how to incentivize these structures to enable data sharing at scale remains largely unexplored. In this paper, we propose incentive mechanisms for decentralized data-sharing platforms. We use smart contracts to automate different payment options between data owners and data requesters. We discuss multiple cost pricing scenarios for data owners to monetize their data. Moreover, we simulate the incentive mechanisms on a blockchain-based data-sharing platform. The evaluation of our simulation indicates that a cost compensation model for the data owner can rapidly cover the cost of data sharing and balance the overall incentives for all the actors in the platform.
Konstantinos Kaltakis, Panagiota Polyzi, George Drosatos, Konstantinos Rantos
Blockchain, a promising technology that has matured and nowadays is widely used in many fields, such as supply chain management, smart grids, agriculture and logistics, has also been proposed for the Internet of Vehicles (IoV) ecosystem to enhance the protection of the data that roadside units and vehicles exchange. Blockchain technology can inherently guarantee the availability, integrity and immutability of data stored in IoV, yet it cannot protect privacy and data confidentiality on its own. As such, solutions that utilise this technology have to consider the adoption of privacy-preserving schemes to address users’ privacy concerns. This paper provides a literature review of proposed solutions that provide different vehicular services using blockchain technology while preserving privacy. In this context, it analyses existing solutions’ main characteristics and properties to provide a comprehensive and critical overview and identifies their contribution in the field. Moreover, it provides suggestions to researchers for future work in the field of privacy-preserving blockchain-enabled solutions for vehicular networks.
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.
Patrick McCorry, Maryam Mehrnezhad, Ehsan Toreini, Siamak F. Shahandashti · 5 authors
This article discusses secure methods to conduct e-voting over a blockchain in three different settings: decentralized voting, centralized remote voting, and centralized polling station voting. These settings cover almost all voting scenarios that occur in practice. A proof-of-concept implementation for decentralized voting over Ethereum’s blockchain is presented. This work demonstrates the suitable use of a blockchain not just as a public bulletin board but, more importantly, as a trustworthy computing platform that enforces the correct execution of the voting protocol in a publicly verifiable manner. We also discuss scaling up a blockchain-based voting application for national elections. We show that for national-scale elections the major verifiability problems can be addressed without having to depend on any blockchain. However, a blockchain remains a viable option to realize a public bulletin board, which has the advantage of being a “preventive” measure to stop retrospective changes on previously published records as opposed to a “detective” measure like the use of mirror websites. CCS Concepts: • Security and privacy ;