Eivind Solberg Rydningen, Erika Ã…sberg, Letizia Jaccheri, Jingyue Li
The merging of Distributed Ledger Technology (DLT) and the Internet of Things (IoT) has opened new opportunities for innovation in health data management. Issues such as security breaches, privacy violations and fragmented data are just some of the problems that DLT might solve. This research investigates how the IOTA Tangle can provide reliable and secure health data management.
Mr. Anuj Mali, Mr. Bharath Shinde, Mr. Sahil Sharma, Mr. Saurabh Khatal · 5 authors
Block chains are now firmly established as a digital technology that combines cryptographic, data management, networking, and incentive mechanisms to support the verification, execution, and recording of transactions between parties. While block chain technologies were originally intended to support new forms of digital currency for easier and secure payments, they now hold great promise as a new foundation for all forms of transactions. Agribusiness stands to become a key beneficiary of this technology as a platform to execute ‘smart contracts’ for transactions, particularly for high-value produce. First it is important to distinguish between private digital currencies and the distributed ledger and block chain technologies that underlie them. The distributed and cross-border nature of digital currencies like Bitcoin means that regulation of the core protocols of these systems by central banks is unlikely to be effective. Monetary authorities are focused more on understanding ‘on-ramps’ and ‘off-ramps’ that constitute the links to the traditional payments system rather than being able to monitor and regulate the currency itself. In contrast to the digital currency feature of block chain, the distributed ledger feature has the potential for widespread use in agribusiness and trade financing, especially where workflows involve many different parties with no trusted central entity.
Mr. Anuj Mali, Mr. Bharath Shinde, Mr. Sahil Sharma, Mr. Saurabh Khatal · 5 authors
Block chains are now firmly established as a digital technology that combines cryptographic, data management, networking, and incentive mechanisms to support the verification, execution, and recording of transactions between parties. While block chain technologies were originally intended to support new forms of digital currency for easier and secure payments, they now hold great promise as a new foundation for all forms of transactions. Agribusiness stands to become a key beneficiary of this technology as a platform to execute ‘smart contracts’ for transactions, particularly for high-value produce. First it is important to distinguish between private digital currencies and the distributed ledger and block chain technologies that underlie them. The distributed and cross-border nature of digital currencies like Bit coin means that regulation of the core protocols of these systems by central banks is unlikely to be effective. Monetary authorities are focused more on understanding ‘on-ramps’ and ‘off-ramps’ that constitute the links to the traditional payments system rather than being able to monitor and regulate the currency itself. In contrast to the digital currency feature of block chain, the distributed ledger feature has the potential for widespread use in agribusiness and trade financing, especially where workflows involve many different parties with no trusted central entity.
Medical data sharing is of great significance in promoting smart medicine. However, the heterogeneity of information systems used by various medical institutions makes sharing difficult. In addition, since medical data involves a great deal of sensitive information, sharing it could easily lead to the leakage of personal privacy. Blockchain, gained popularity as a distributed ledger technology, has great potential to connect heterogeneous systems and provides authenticity and integrity guarantees for medical data sharing. Focusing on the issues of medical data sharing and privacy protection, we propose a medical data sharing scheme based on consortium blockchain. To achieve access control, attribute-based access control technique is implemented, where patients preset attribute-specific access policies for their medical records, and record requesters are described by a set of attributes. For patients, we devise a hybrid storage mode to write access policies of medical records on the consortium blockchain network and store encrypted medical records off-chain. Leveraging blockchain and smart contracts, access privilege control and access history tracking can be realized. To enhance the key management, a tree of medical records is constructed for each patient, and by simply keeping the medical record trees, patients can recover their encryption keys at any time. Furthermore, we carry out an extensive analysis to show the high security and efficiency of our proposed scheme. Finally, we build a Quorum consortium blockchain on the Tencent Cloud and deploy smart contracts on the chain to simulate transactions in our scheme. The experiment results indicate the proposed scheme achieves good feasibility.
Simon Tschirner, Shashank Tripathi, Mathias Roeper, Markus M. Becker · 5 authors
Blockchains provide environments where parties can interact transparently and securely peer-to-peer without needing a trusted third party. Parties can trust the integrity and correctness of transactions and the verifiable execution of binary code on the blockchain (smart contracts) inside the system. Including information from outside of the blockchain remains challenging. A challenge is data privacy. In a public system, shared data becomes public and, coming from a single source, often lacks credibility. A private system gives the parties control over their data and sources but trades in positive aspects as transparency. Often, not the data itself is the most critical information but the result of a computation performed on it. An example is research data certification. To keep data private but still prove data provenance, researchers can store a hash value of that data on the blockchain. This hash value is either calculated locally on private data without the chance for validation or is calculated on the blockchain, meaning that data must be published and stored on the blockchain -- a problem of the overall data amount stored on and distributed with the ledger. A system we called moving smart contracts bypasses this problem: Data remain local, but trusted nodes can access them and execute trusted smart contract code stored on the blockchain. This method avoids the system-wide distribution of research data and makes it accessible and verifiable with trusted software.
Jianbin Wu, Sami Ahmed Haider, Manish Bhardwaj, Aditi Sharma · 5 authors
Recently, data integrity for multiagent-based big data environments has been challenging. This paper presents a blockchain-based Merkle DAG structure (M-DAG) for audit data integrity. M-DAG resolves the problem that arises due to the multicopy of a large data volume in a big data environment. It employed Boneh–Lynn–Shacham’s (BSL) signature to verify the integrity of identical multicopy on big data environments. The proposed M-DAG audit mechanism uses a consortium chain algorithm for decentralized traceability and audit to archive reliable data. The evaluation has been carried out for the efficiency of the data integrity audit.
The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.
Hongze Liu, Jie Li, Shijing Yuan, Wenqi Cao · 5 authors
Hierarchical Federated Learning (HFL) is introduced as a promising technique that allows model owners to fully exploit computational resources and bandwidth resources to train the global model. However, due to the high training cost, a single model owner may not be able to deploy HFL. To address this issue, we develop a smart contract based trust crowdfunding mechanism for HFL, which enables multiple model owners to obtain a crowdfunding model with high social utility for multiple crowdfunding participants. To ensure the authenticity of the crowdfunding mechanism, we implemented the Vickey-Clark-Croves (VCG) mechanism to encourage all crowdfunding participants and clients to provide realistic bids and offers. At the same time, in order to ensure guaranteed trustworthiness of crowdfunding and automatic distribution of funds, we develop and implement a smart contract to record the crowdfunding process and training results in the blockchain. We prove that the proposed scheme satisfies the budget balance and participant constraint. Finally, we implement a prototype of this smart contract on an Ethereoum private chain and evaluate the proposed VCG mechanism. The experimental results demonstrate that the proposed scheme can effectively improve social utility while ensuring the authenticity and trustworthiness of the crowdfunding process.
Spatial crowdsourcing emerges as a new computing paradigm that enables mobile users to accomplish spatio- temporal tasks in order to solve human-intrinsic problems. Existing crowdsourcing systems critically use centralized servers for interacting with workers and making task assignment decisions. These systems are hence susceptible to issues such as the single point of failure and the lack of operational transparency. Prior work, therefore, turns to blockchain-based decentralized crowdsourcing systems, yet still suffers from problems of lacking efficient task assignment scheme, requiring a deposit to an untrusted system, low block generation speed, and high transaction fees. To address these issues, we design a blockchain-based decentralized framework for spatial crowdsourcing, which we call SC-EOS. Our system does not rely on any trusted servers, while providing efficient and user-customizable task assignment, low monetary cost, and fast block generation. More importantly, it frees users from making a deposit into an untrusted system. Our framework can also be extended and applied to generic crowdsourcing systems. We implemented the proposed system on the EOS blockchain. Trace-driven evaluations involving real users show that our system attains the comparable task assignment performance against a clairvoyant scheme. It also achieves 10× cost savings than an Ethereum-based implementation.
Lianshan Sun, Xue Bai, Chao Zhang, Yang Li · 6 authors
In the Big Data era, data provenance has become an important concern for enhancing the trustworthiness of key data that are rapidly generated and shared across organizations. Prevailing solutions employ authoritative centers to efficiently manage and share massive data. They are not suitable for secure and trustworthy decentralized data provenance sharing due to the inevitable dishonesty or failure of trusted centers. With the advent of the blockchain technology, embedding data provenance in immutable blocks is believed to be a promising solution. However, a provenance file, usually a directed acyclic graph, cannot be embedded in blocks as a whole because its size may exceed the limit of a block, and may include various sensitive information that can be legally accessed by different users. To this end, this paper proposed the BSTProv, a blockchain-based system for secure and trustworthy decentralized data provenance sharing. It enables secure and trustworthy provenance sharing by partitioning a large provenance graph into multiple small subgraphs and embedding the encrypted subgraphs instead of raw subgraphs or their hash values into immutable blocks of a consortium blockchain; it enables decentralized and flexible authorization by allowing each peer to define appropriate permissions for selectively sharing some sets of subgraphs to specific requesters; and it enables efficient cross-domain provenance composition and tracing by maintaining a high-level dependency structure among provenance graphs from different domains in smart contracts, and by locally storing, decrypting, and composing subgraphs obtained from the blockchain. Finally, a prototype is implemented on top of an Ethereum-based consortium blockchain and experiment results show the advantages of our approach.
Mikael Beyene, Philipp A Toussaint, Scott Thiebes, Matthias Schlesner · 6 authors
OBJECTIVE: Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. MATERIALS AND METHODS: We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. RESULTS: Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. DISCUSSION: Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people's attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. CONCLUSION: While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners.
Blockchains currently exist in silos, competing when they could be cooperating. Interoperability is essential to allow for communication between them and motivate mass adoption. In permissioned blockchains, interoperability is harder given their opaqueness. The solutions proposed so far to address interoperability require a trusted private third party, which may be insecure and is not ideal. We propose T-ODAP, a secure multi-layered protocol that enables a trustless solution for permissioned blockchain interoperability, eliminating the need for trust in the protocol’s participants. It provides a Decentralized View Storage, a connector that connects permissioned blockchains to the latter, and a trustless version of the ODAP protocol. T-ODAP models the participants as rational agents using game theory techniques and is implemented using \textit{Polkadot} and \textit{Hyperledger Cactus}. We tested the implemented solution, evaluated the system’s robustness in face of attacks, and concluded that the system is \textit{(k,t)-weak-robust}.
Truc Nguyen, Phuc Thai, Tre’ R. Jeter, Thang N. Dinh · 5 authors
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.
Centralization has dominated classic scientific, social, and economic developments. Decentralization has also received increasing attention in management, decision, governance, and economics, despite its incomparability in AI. Going beyond centralized and distributed AI, this article reviews and delineates the conceptual map, research issues, and technical opportunities of decentralized AI and edge intelligence. The complementarity and metasynthesis between centralized and decentralized AI are also elaborated. We further assess where decentralized AI and edge intelligence can enable and promote smart blockchain, Web3, metaverse and decentralized science disciplinarily, technically, practically, and more broadly.
The Coronavirus disease 2019 has manifested into a global pandemic spreading into almost all the countries and territories in the world. Contact tracing, followed by testing and isolation, have been identified as important tools in containing the proliferation of the disease. Because of manual contact tracing limitations, smartphone apps for digital contact tracing have been deployed by authorities in multiple countries. However, many of these apps have faced criticism because of interoperability, privacy and security issues. This paper proposes an open architecture based on blockchain technology that addresses some of these criticisms. It enables interoperability through a publicly-readable consortium blockchain database, preserving privacy by distributing users’ partial identities among multiple decentralised authorities and providing security through digital signature and asymmetric key encryption. In addition, the architecture provides a fast proof-of-authority based consensus algorithm using reputation as a stake to add and validate blocks on the blockchain efficiently and a swift contact tracing algorithm using Spatio-temporal and key-valued databases.
Yue Wang, Tingyu Che, Xiaohu Zhao, Tao Zhou · 6 authors
Due to the competitive relationship among different smart factories, equipment manufacturers cannot integrate the private information of all smart factories to train the intelligent manufacturing equipment fault prediction model and improve the accuracy of intelligent manufacturing equipment fault detection. The use of a low fault recognition rate model for smart factories will cause additional losses for them. In this work, we propose a blockchain-based privacy information security sharing scheme in Industrial Internet of Things (IIoT) to solve the sharing problem of private information in smart factories. Firstly, we abstract smart factories as edge nodes and build decentralized, distributed trusted blockchain networks based on Ethereum clients on simulated edge devices and propose an Intelligent Elliptic Curve Digital Signature Algorithm (IECDSA) to guarantee the ownership of shared information by edge nodes. Secondly, we propose the Reputation-based Delegated Proof of Stake (RDPoS) consensus algorithm to improve the security and reliability of the Delegated Proof of Stake (DPoS) consensus algorithm. Furthermore, we design and implement an incentive mechanism based on information attributes to increase the motivation of edge nodes to share information. Finally, the proposed solution is simulated. Through theoretical and simulation experiments, it is proved that the blockchain-based privacy information security sharing scheme in IIoT can improve the enthusiasm of edge nodes to share information on the premise of ensuring the security of information sharing.
Digital Health Passes (DHP), systems of digitally validating quarantine and vaccination status such as the New York IBM Excelsior Pass, demonstrate a lawful means to approach some benefits offered by "true elimination" treatment strategies-which focus on the complete elimination of cases instead of investing more in controlling the progression of the disease-of COVID-19. Current implementations of DHPs require region-based control and central storage of Protected Health Information (PHI)-creating a challenge to widespread use across different jurisdictions with incompatible data management systems and a lack of standardized patient privacy controls. In this work, a mechanism for decentralized PHI storage and validation is proposed through a novel two-stage handshaking mechanism update to blockchain proof-of-stake consensus. The proposed mechanism, when used to support a DHP, allows individuals to validate their quarantine and testing universally with any jurisdiction while allowing their right of independent movement and the protection of their PHI. Implementational details on the protocol are given, and the protocol is shown to withstand a 1% disturbance attack at only 923 participants via a Monte-Carlo simulation: further validating its stability.
In the last two years, due to the pandemic and restrictive measures, the dependence of music creators and artists on the Internet, where they could promote their work, organize live streaming concerts, and talk to the public, has increased and expanded even more and seeks higher revenue from digital music platforms. An important issue that arises from the above statement is protecting the authors' copyright regarding the uses and sharing in the digital services of their works with protected content. Although circulated in digital information, the protected content is not information but a product of ethical and commercial value. While it has an intangible owner and it owes its existence to the creative idea of its creator, it is not an idea. The imposition of legal and commercial conditions on its movement cannot be associated with any restrictions on the free movement of information, as it is not related to them. In general, the unauthorized exchange of digital music files via peer-to-peer violates copyright law. The exchange of files is unauthorized, as it does not have the relevant permission from the creators and beneficiaries and is therefore illegal. With this in mind, this paper proposes a highly effective way of protecting the copyright of music technology, which is based on the widespread use of artificial intelligence, blockchain, and cryptography technologies. Specifically, an advanced blockchain model based on Hyperledger Fabric is introduced, which, however, uses Quantum Homomorphic Encryption and Quantum Zero-Knowledge Arguments. Music files are implemented as Nonfungible Tokens (NFTs), which activate smart contracts. Finally, an advanced collaborative filtering algorithm provides recommendations for effectiveness in securing the copyrights of music industry creators. A specialized scenario was built to model the proposed system to verify the degree of protection on music intellectual property in developing a security simulation with an innovative consensus-based zero knowledge and the quantum fully homomorphic encryption technique. Experiment results show that this technique can aid in implementing a technologically aware system capable of providing a powerful answer to a current real-world problem.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammad Saidur Rahman, Mohammed Atiquzzaman · 6 authors
This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.
Vehicular Ad-hoc NETworks (VANETs), a special kind of Mobile Ad-hoc NETworks (MANETs), play an important role in Intelligent Transportation Systems (ITS). Via wireless technology, vehicles exchange information related to road conditions and their status, and, thereby, VANETs enhance transportation safety and efficiency. A critical aspect of VANETs is providing privacy for the vehicles. The employment of pseudonym certificates is a well-known solution to the privacy problems in VANETs. However, certificate management faces challenges in renewing certificates and revoking vehicles. The centralized certificate management, especially resulting in the delay of the revocation process, harms the nodes of VANETs. This paper proposes a blockchain structure-based certificate management for VANETs and voting-based revocation to halt misbehaving vehicles’ actions. Moreover, this paper presents extended privacy for the participants of the voting process using ring signatures.
Miners of a blockchain exchange information about blocks and transactions with one another via a peer-to-peer (P2P) network. The speed at which they learn of new blocks and transactions in the network determines the likelihood of forks in the chain, which in turn has implications for the efficiency as well as security of proof-of-work (PoW) blockchains. Despite the importance of information propagation delays in a blockchain's peer-to-peer network, little is known about them. The last known empirical study was conducted, for instance, by Decker and Wattenhofer in 2013 [11].
Storage is a promising application for permission-less blockchains. Before blockchain, cloud storage was hosted by a trusted service provider. The centralized system controls the permission of the data access. In web3, users own their data. Data must be encrypted in a permission-less decentralized storage network, and the permission control should be pure cryptographic. Proxy re-encryption (PRE) is ideal for cryptographic access control, which allows a proxy to transfer Alice’s ciphertext to Bob with Alice’s authorization. The encrypted data are stored in several copies for redundancy in a permission-less decentralized storage network. The redundancy suffers from the outsourcing attack. The malicious resource provider may fetch the content from others and respond to the verifiers. This harms data integrity security. Thus, proof-of-replication (PoRep) must be applied to convince the user that the storage provider is using dedicated storage. PoRep is an expensive operation that encodes the original content into a replication. Existing PRE schemes cannot satisfy PoRep, as the cryptographic permission granting generates an extra ciphertext. A new ciphertext would result in several expensive replication operations. We searched most of the PRE schemes for the combination of the cryptographic methods to avoid transforming the ciphertext. Therefore, we propose a new PRE scheme. The proposed scheme does not require the proxy to transfer the ciphertext into a new one. It reduces the computation and operation time when allowing a new user to access a file. Furthermore, the PRE scheme is CCA (chosen-ciphertext attack) security and only needs one key pair.
Kun Li, Huachun Zhou, Zhe Tu, Feiyang Liu · 5 authors
The malicious flow originating from massive access devices in 6G network will increase sharply. In order to effectively reduce malicious flow, we hope to establish a new framework for coordination of security monitoring and malicious behaviour control in 6G network. Federated learning provides data and privacy protection for the distributed network security behaviour knowledge base. However, since the equipment of its participants needs to upload the original data to the central server for model training, this may lead to data leakage in the knowledge base. Therefore, in this article, we first use the knowledge graph to describe network security behaviours, then build a universal network security malicious behaviour knowledge base, and discuss its application scenarios. Then, we propose a blockchain empowered federated learning (BeFL) for distributed network security malicious behaviour knowledge base architecture to ensure the security of knowledge transmission. Finally, we deployed the designed distributed knowledge base in the prototype system and compared it with the other two baseline methods to verify the performance. Relevant results show that our method outperforms other methods in terms of user identification, flow detection, and attack source tracing.