Abstract. Data sharing and collaboration are critical to solving large scale problems. The prevailing soil data-sharing model is based on different groups sending their data to a lead party. This model is of a centralised nature and, consequently, results in the participants ceding their control and governance over their data to the lead party. Here we explore the use of a distributed ledger (blockchain) to solve the aforementioned issues. We explain what a blockchain is and some of its characteristics to then describe some features of a blockchain that makes it an interesting candidate for an inter-institutional database. Finally, we describe the potential use case of developing a global soil spectral library with multiple, independent international institutions constituting the network.
Bin Cao, Mengyang Li, Lei Zhang, Yixin Li · 5 authors
The impact of communication transmission delay on the original blockchain, has not been well considered and studied since it is primarily designed in stable wired communication environment with high communication capacity. However, in a wireless scenario, due to the scarcity of spectrum resource, a blockchain user may have to compete for wireless channel to broadcast transactions following media access control (MAC) mechanism. As a result, the communication transmission delay may be significant and pose a bottleneck on the blockchain system performance and security. To facilitate blockchain applications in wireless industrial Internet of Things (IIoTs), this article aims to investigate whether the widely used MAC mechanism, carrier sense multiple access/collision avoidance (CSMA/CA), is suitable for wireless blockchain networks or not. Based on tangle, as an example to analyze the system performance in term of confirmation delay, transaction per second and transaction loss probability by considering the impact of queueing and transmission delay caused by CSMA/CA. Next, a stochastic model is proposed to analyze the security issue taking into account the malicious double-spending attack. Simulation results provide valuable insights when running blockchain in wireless network, the performance would be limited by the traditional CSMA/CA protocol. Meanwhile, we demonstrate that the probability of launching a successful double-spending attack would be affected by CSMA/CA as well.
Blockchain technology is currently one of the most popular topics in the field of information technology. Blockchain is often associated with cryptocurrency or electronic cash, but it can also be extended to any interconnected information blocks. Now blockchain finds application in areas such as financial transactions, user identification, or the creation of cybersecurity technologies. So, in this framework we shall talk about that despite the fact that blockchain technology is reliable and supportive, the security issues and challenges of this technology cannot be left out especially when it comes to building personal privacy protection.
2 source records
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.
A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain driven, FL environment that is inherently decentralized. BAFFLE leverages Smart Contracts (SC) to coordinate the round delineation, model aggregation and update tasks in FL. BAFFLE boosts computational performance by decomposing the global parameter space into distinct chunks followed by a score and bid strategy. In order to characterize the performance of BAFFLE, we conduct experiments on a private Ethereum network and use the centralized and aggregator driven methods as our benchmark. We show that BAFFLE significantly reduces the gas costs for FL on the blockchain as compared to a direct adaptation of the aggregator based method. Our results also show that BAFFLE achieves high scalability and computational efficiency while delivering similar accuracy as the benchmark methods.
Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.
The intelligent vehicle (IV) has become a promising technology that could revolutionize our life in smart cities sooner or later. However, it yet suffers from many security vulnerabilities. Traditional security methods are incapable to secure the IV data sharing against malicious attacks. Blockchain, as expected by both research and industry communities, has emerged as a good solution to address these issues. The major issues in IV data sharing are trust, data accuracy and reliability of data sharing in the communication channel. Blockchain technology, previously working for the cryptocurrency, has recently applied to build trust and reliability in peer-to-peer networks with similar topologies of IV data sharing. In this chapter, we present a new framework, namely biometric blockchain (BBC), for secure IV data sharing. In our new scheme, biometric information is exploited as a cue to record who is responsible in the data sharing activities, while the proposed BBC technology serves as the backbone of the IV data-sharing architecture. Hence, the proposed BBC technology provides a more reliable trust environment between the vehicles while personal identities are traceable in the proposed new scheme.
Abstract Vector commitment (VC) schemes allow committing to an ordered sequence of ${q}$ values ${(m_1,\cdots ,m_q)}$ in such a way that one can later open the commitment at specific positions. However, the existing VC schemes suffer from two substantial shortcomings that limit their use: (i) the commitments cannot be opened except at some specific positions, and (ii) their security only captures position-binding but offers no privacy: the client may learn additional information about the committed sequence through the proofs and the commitments. To resolve these problems, we first extend VC to a more expressive primitive called VC with sum binding (VCS), in which the commitment can also be opened to the sum of all elements in the committed sequence. VCS additionally satisfies the security of sum binding, which guarantees that the commitment cannot be opened to different sums. To enhance its privacy, we extend VCS to zero-knowledge VCS (ZKVCS), in which commitments and proofs constructed during the protocol execution leak nothing about the committed sequence. We formalize this new property by a standard real/ideal experiment. Meanwhile, the detailed performance analyses and simulations show that our proposed schemes are more practical. Finally, we introduce a novel notion of (zero-knowledge) verifiable database supporting sum and show how to construct it from our (ZK)VCS scheme.
Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.
Ajay Kumar Shrestha, Ralph Deters, Julita Vassileva
The tremendous technological advancement in the last few decades has brought many enterprises to collaborate in a better way while making intelligent decisions. The use of Information Technology tools in obtaining data of people's everyday life from various autonomous data sources allowing unrestricted access to user data has emerged as an important practical issue and has given rise to legal implications. Various innovative models for data sharing and management have privacy and centrality issues. To alleviate these limitations, we have incorporated blockchain in user modeling. In this paper, we constructed a decentralized data sharing architecture with MultiChain blockchain in the travel domain, which is also applicable to other similar domains including education, health, and sports. Businesses that operate in the tourism industries including travel and tour agencies, hotels and resorts, shopping malls are connected to the MultiChain and they share their user profile data via stream in the MultiChain. The paper presents the hotel booking service for an imaginary hotel as one of the enterprise nodes, which collects user profile data with proper validation and will allow users to decide which of their data to be shared thus ensuring user control over their data and the preservation of privacy. The data from the repository is converted into an open data format while sharing via stream in the blockchain so that other enterprise nodes, after receiving the data, can easily convert them and store into their own repositories. The paper presents an evaluation of the performance of the model by measuring the latency and memory consumption with three test scenarios that mostly affect the user experience. The node responded quickly in all of these cases.
Xiaolong Xu, Yi Chen, Xuyun Zhang, Qingxiang Liu · 6 authors
Summary Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth‐generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain‐based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain‐based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA‐III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO.
Yue Zhang, Jian Weng, Jiasi Weng, Ming Li · 5 authors
With the popularity of Blockchain comes grave security-related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. This paper studies how to provide a more general solution for this open question. Concretely, we propose Onionchain, featuring a suite of protocols, offering both traceability and privacy. As the term implies, our Onionchain is inspired by Onion routing. We investigate the principles of Onion routing carefully and integrate its mechanism together with Blockchain technology. We advocate the Blockchain community to adopt Onionchain with the regards of privacy and traceability. To this end, a case-study of Onionchain, which runs in the context of Vehicular Ad Hoc Networks (VANETs), is proposed, providing the community a guideline to follow. Systematic security analysis and extensive experiments are also conducted to validate our secure and cost-effective Onionchain.
Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a user and each directed, weighted edge represents funds escrowed on a blockchain; these funds can be transacted only between the endpoints of the edge. Users efficiently transmit funds from node A to B by relaying them over a path connecting A to B, as long as each edge in the path contains enough balance (escrowed funds) to support the transaction. Whenever a transaction succeeds, the edge weights are updated accordingly. In deployed PCNs, channel balances (i.e., edge weights) are not revealed to users for privacy reasons; users know only the initial weights at time 0. Hence, when routing transactions, users typically first guess a path, then check if it supports the transaction. This guess-and-check process dramatically reduces the success rate of transactions. At the other extreme, knowing full channel balances can give substantial improvements in transaction success rate at the expense of privacy. In this work, we ask whether a network can reveal noisy channel balances to trade off privacy for utility. We show fundamental limits on such a tradeoff, and propose noise mechanisms that achieve the fundamental limit for a general class of graph topologies. Our results suggest that in practice, PCNs should operate either in the low-privacy or low-utility regime; it is not possible to get large gains in utility by giving up a little privacy, or large gains in privacy by sacrificing a little utility.
Fan Zhang, Deepak Maram, Harjasleen Malvai, Steven Goldfeder · 5 authors
Thanks to the widespread deployment of TLS, users can access private data over channels with end-to-end confidentiality and integrity. What they cannot do, however, is prove to third parties the {\em provenance} of such data, i.e., that it genuinely came from a particular website. Existing approaches either introduce undesirable trust assumptions or require server-side modifications. As a result, the value of users' private data is locked up in its point of origin. Users cannot export their data with preserved integrity to other applications without help and permission from the current data holder. We propose DECO (short for \underline{dec}entralized \underline{o}racle) to address the above problems. DECO allows users to prove that a piece of data accessed via TLS came from a particular website and optionally prove statements about such data in zero-knowledge, keeping the data itself secret. DECO is the first such system that works without trusted hardware or server-side modifications. DECO can liberate data from centralized web-service silos, making it accessible to a rich spectrum of applications. To demonstrate the power of DECO, we implement three applications that are hard to achieve without it: a private financial instrument using smart contracts, converting legacy credentials to anonymous credentials, and verifiable claims against price discrimination.
Cloud storage today depends entirely on large storage providers. Such storage providers function as untrusted third parties that process data for storing, sending and receiving data from an entity. This style of system has many problems, such as high operating costs, software quality and data security. In this paper, we present a model of a multi-user access control system for databases that use blockchain technology to provide stable, distributed data processing. The system allows the data owner to upload the data via a web portal. So, the user who has the secret key to the particular data that has been uploaded to Cloud in encrypted form can only access the folder. Eventually, the system promotes data privacy by maintaining the immutability of the blockchain by processing it in the cloud. We have proposed a secure, blockchain-based data storage and access control system to increase the security of cloud storage.
In the era of the Internet, a user can perform some actions by being assigned with a pseudo identity, and most users' identities are stored by the certified third parties. This centralized identity management model has a potential safety concern. Once the database server is crashed or the data are leaked, the privacy of the user are exposed. In this paper, a smart contract technology on the blockchain is used to build a decentralized identity management system. The system is usercentric, allowing users to fully control their identity information, only authorized third parties allow access to their user information. Attribute-based authentication is used to achieve identity anonymity. In addition, a reputation model based on attribute reputation is proposed, which makes the user's identity credible even in a decentralized environment.
Jawaid Iqbal, Arif Iqbal Umar, Noorul Amin, Abdul Waheed
In body sensor networks, both wearable and implantable biosensors are deployed in a patient body to monitor and collect patient health record information. The health record information is then transmitted toward the medical server via a base station for analysis, diagnosis, and treatment by medical experts. Advancement in wireless technology although improves the patient health–monitoring mechanism, but still there are some limitations regarding security, privacy, and efficiency due to open wireless channel and limited resources of body sensor networks. To overcome these limitations, we have proposed an efficient and secure heterogeneous scheme for body sensor networks, in which biosensor nodes use a certificate-less cryptography environment to resolve the key escrow and certificate-management problems, while MS uses a public key infrastructure environment to enhance the scalability of the networks. Furthermore, we design an online/offline signcryption method to overcome the burden on biosensor nodes. We split the signcryption process into two phases: offline phase and online phase. In the offline phase, the major operations are computed without prior knowledge of patient data. While in online phase, the minor operations are computed when patient data are known. Besides, we have used a new hybrid blockchain technology approach for the secure transmission of patient information along with attributes stored in the medical server toward the cloud that provides ease of patient data access remotely from anywhere by the authorized users and data backup in case of medical server failure. Moreover, hybrid blockchain provides advantages of interoperability, transparency traceability, and universal access. The formal security analysis of the proposed scheme is proved in the standard model, and informal security assures that our scheme provides resistance against possible attacks. As compared to other existing schemes, our proposed scheme consumes fewer resources and efficient in terms of processing cost, transmission overhead, and energy consumption.
Recently, due to the popularity of Bitcoin, interest in the blockchain which is the core technology of Bitcoin, has also increased. Blockchain is a distributed ledger technology that stores transaction information that occurs in P2P(Peer-to-Peer) networks on the ledger of all nodes in a different way than centralized method and verifies whether they are stored correctly. As a result of blockchain technology, not only Bitcoin but also various cryptocurrencies such as Ethereum, Litecoin, Ripple and Bitcoin Cash are being developed. Blockchain is used in various fields because of its features such as integrity and anonymity, but it is also used for illegal transactions and drug transactions. To solve these problems, monitoring system is needed to collect data from blockchain networks and detect abuse and illegal transactions. In this paper, we propose monitoring system for detecting and tracking illegal transactions by collecting and analyzing information in a blockchain network. In addition, we introduce an efficient storage system implemented by Apache Kafka and Apache Storm among the Blockchain monitoring systems.
Federated learning (FL) is a decentralized learning method that deviated from the conventional centralized learning. The FL progresses learning locally on each device and gradually improves the learning model through interaction with the central server. However, it can cause network overload because of limited communication bandwidth and the participation of a huge number of users. One of the ways to minimize the network load is for the model to converge rapidly and stably with target learning accuracy. In this paper, we propose blockchain based federated learning scenario. Blockchain can efficiently induce users to participate in learning and can separate each participating user as a `node'. In addition, it can be pursued the integrity, stability, and so on. We consider two types of weights to choose the subset of clients for updating the global model. First, we consider the weight based on local learning accuracy of each client. Second, we consider the weight based on participation frequency of each client. We choose two key performance indicators, learning speed and standard deviation, to compare the performance of our proposed scheme with existing schemes. The simulation results show that our proposed scheme achieves higher stability along with fast convergence time for targeted accuracy compared to others.
In recent years, the incredible growth and diversity of IoT systems and applications have generated an enormous amount of sensing data which play an essential role in IoT-based smart systems. So far, it has consumed much time and cost to collect these sensing data enough for these intelligent systems, which is the main reason leading to the needs of sharing available data to shorten time-consuming and cost savings for the data collection process. However, there are many challenges in ensuring integrity, security, and fairness in the data sharing process. In this paper, the authors propose a model leveraging the emerging Blockchain technology as an alternative solution to enhance the security of IoT data sharing management in terms of three principal criteria including confidentiality, integrity, and availability. Our prototype on Ethereum Blockchain demonstrates the feasibility of the proposed model.
In this paper, we propose blockchain network based architecture called “FLchain” for enhancing security of Federated Learning (FL). We leverage the concept of channels for learning multiple global models on FLchain. Local model parameters for each global iteration are stored as a block on the channel-specific ledger. We introduce the notion of “the global model state trie” which is stored and updated on the blockchain network based on the aggregation of local model updates collected from mobile devices. Qualitative evaluation shows that FLchain is more robust than traditional FL schemes as it ensures provenance and maintains auditable aspects of FL model in an immutable manner.