The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks (6G). Due to data heterogeneity and dynamics, decentralized data management (DM) is desirable for achieving transparent data operations across network domains, and blockchain can be a promising solution. However, the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency, efficiency, and privacy requirements in decentralized blockchain-based DM. In this paper, we investigate blockchain solutions to address the challenge. First, we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures. Second, we investigate the authentication and authorization requirements for DM stakeholders. Third, we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing. Subsequently, we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives. Finally, we conclude this paper and discuss future research directions.
Contemporarily, the fast development of computing, communication, and storage technology has revolutionized the way that various data-based applications reach massive data from underlying sensor networks. However, such a process also raises two challenging but critical issues: 1) trustworthy and 2) privacy issue for data collectors. Therefore, this article proposes a novel system, which is designed over the distributed mobile-edge network to sufficiently exploit advantages of blockchain and differential privacy (DP) to collect trustworthy data and protect privacy for data collectors. First, to improve trustworthiness of data collections, a new consensus mechanism is proposed for blockchain-based data collection structure, which comprehensively incorporates trustworthy, collection contribution, and throughput together to prefer data collectors for the next block. Second, with the assistance of fully trusted devices, a verifiable trustworthy evaluation strategy is designed to accurately compute the trustworthiness for data collectors. Third, we enforce DP on the data stored in a global blockchain maintained by the cloud server to protect privacy for data collectors without influencing data availability. Finally, both theoretical analyses and experimental results prove that the proposed system comprehensively improves performance of data collections in distributed network without adding any additional cost for the cloud server, compared to other schemes.
As a distributed computing paradigm, edge computing has become a key technology for providing timely services to mobile devices by connecting Internet of Things (IoT), cloud centers, and other facilities. By offloading compute-intensive tasks from IoT devices to edge/cloud servers, the communication and computation pressure caused by the massive data in Industrial IoT can be effectively reduced. In the process of computation offloading in edge computing, it is critical to dynamically make optimal offloading decisions to minimize the delay and energy consumption spent on the devices. Although there are a large number of task offloading-decision models, how to measure and evaluate the quality of different models and configurations is crucial. In this article, we propose a novel simulation platform named ChainFL, which can build an edge computing environment among IoT devices while being compatible with federated learning and blockchain technologies to better support the embedding of security-focused offloading algorithms. ChainFL is lightweight and compatible, and it can quickly build complex network environments by connecting devices of different architectures. Moreover, due to its distributed nature, ChainFL can also be deployed as a federated learning platform across multiple devices to enable federated learning with high security due to its embedded blockchain. Finally, we validate the versatility and effectiveness of ChainFL by embedding a complex offloading-decision model in the platform, and deploying it in an Industrial IoT environment with security risks.
Purpose This study aims to understand the benefits and challenges associated with the adoption of a blockchain-based identity management system in public services by conducting an academic literature review, and to explore the design of such a system that can be applied to the Korean government. Design/methodology/approach This study explores the adoption of a blockchain-based identity management system using a literature review and an actual design case intended for use by the government sector. Findings Blockchain-based identity management systems can significantly improve transparency, accountability, and reliability in the user control of one's own data while reducing the time and cost needed to deliver public services, as well as increasing administrative efficiency. However, it is not always easy to implement such systems, and introducing new technologies in the government field requires a complicated, time-consuming process. There is currently an appetite for research extending beyond the typical technology-driven approach to elucidate the government adoption of new technologies and explore its implications. Practical implications The idea behind this system is that by storing and managing personal information on the blockchain and providing mobile apps to customers, users can log in or retrieve previously authenticated personal information without having to go through an authentication process. Since users do not need to go through the verification process every time, it is expected that they will be able to access only the necessary personal information more quickly and conveniently without having to deal with unnecessary details. In addition, the blockchain-based operation of a public service effectively increases the transparency and reliability of that service and reduces the social costs caused by personal information leakage. Originality/value This study introduces the design of a blockchain-based identity management system that can be used in public services, specifically in the Korean government sector for the first time. Along with a literature review, the implications that this study gleans from these real-world use cases can contribute to this field of research.
Beatriz Soret, Lam Duc Nguyen, Jan Seeger, Arne Brรถring ยท 10 authors
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multiple iIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in energy-efficient iIoTe and a roadmap to address the open research challenges.
Ming Li, Jian Weng, Jia-Nan Liu, Xiaodong Lin ยท 5 authors
With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy preservation while providing secure data access control in VDF is a nontrivial challenge. To mitigate this issue, in this article, we propose a blockchain-based decentralized solution for VDF named BB-VDF, in which the accountable protocols and privacy-preserving algorithm are constructed. The desirable security properties and fine-grained data access control are achieved based on smart contract and the customized cryptographic construction. Specifically, we design a distributed key-policy attribute-based encryption scheme with partially hidden access structures, named DKP-ABE-H, to realize the secure fine-grained forensics data access control. Further, a novel smart contract is designed to model the forensics procedures as a finite state machine, which guarantees accountability that each participant performs auditable cooperation under tamper resistant and traceable transactions. Systematic security analysis and extensive experimental results show the feasibility and practicability of our proposed BB-VDF scheme.
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Oct 1, 2021ยท2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Feng Xiaohua, Marc Conrad, Eze C Elias, Khalid Hussein
AI (Artificial intelligence) application on Big Data had been developed fast. AI cyber security defense for the facing threats were required. Blockchain technology was invented in 2008 with BTC (Bit coin. This technology could be benefited alongside the custom of Blockchain, AI, Big Data and so on. There were a rapid progress in the advancement of Blockchain. This subject had recently become a discussion topic in the ICT (Information and Communications Technology) world. In this paper, AI security is discussed from the initial stage. Suggestion: In this paper, we discussed the impact of AI security from the initial stage and its impact and benefits to IT engineers, ICT students and CS (Computer Sciences) academic researchers, using a case study of medical records with personal recognizable identification privacy information that needs strict access control security. We considered its need for trustworthy cyber security, anti-fake, anti-alteration and transaction accounting transparency reputation to be applied to the NHS (National Health Service). Lastly, the paper provided some necessarily analysis. Blockchain technology had trustworthy cyber security, anti-fake, anti-alteration and transaction accounting transparency reputation to be considered to be applied to NHS (National Health Service). This short paper provided some analysis necessarily.
Crowdsourcing relies on Internet-wide capability to solve the complicated or large-scale tasks that are difficult to accomplish separately by individuals. However, traditional centralized crowdsourcing systems highly depend on the centralized coordination server to operate, making it extremely vulnerable to the single-point bottleneck and failure. And the whole system lacks verifiable trustworthiness among the participants. This paper proposes a blockchain-based framework for the distributed crowdsourcing without relying solely on any single trusted entity. The solutions for the outsourced tasks are verified with consensus among the participants with a reputation mechanism. We prove by theoretical security analysis that the proposed scheme resists malicious attacks better comparing to other typical crowdsourcing schemes. A prototype system is implemented based on Ethereum to demonstrate the overhead performance in various aspects. Theoretical and experimental evaluations show that the proposed scheme possesses reliability, security, quality, and feasibility.
Oct 1, 2021ยท2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Manxiang Yang, Baopeng Ye, Yuling Chen, Tao Li ยท 6 authors
There are some problems such as behavior deception and service swing while utilizing distributed k-anonymity technology to construct an anonymous domain. If the malicious nodes submit a false location coordinate to take participate in the process of constructing, the coordinate of the honest node will be leaked, and the attacker will increase the reputation illegally. To tackle those problems, we propose a de-swinging scheme based on the smart contract for location privacy protection. Primarily, a de-swinging reputation evaluation method (DREM) is introduced, in which participants prefer to choose nodes with high reputations to complete the construction process of the anonymous domain. In this method, we design a penalty factor to curb malicious behavior. Besides, based on our proposed DREM, a cridible cloaking area is constructed to protect the location privacy of the request nodes. We utilize smart contract to calculate reputation automatically during the process of construction. Finally, the security analysis and simulation results indicate that our proposed scheme can effectively resist malicious attacks, detect malicious nodes quickly, encourage nodes to participate honestly in the construction of anonymous domain.
Abstract This chapter first introduces the fundamental principles of blockchain and the integration of blockchain and mobile edge computing (MEC). Blockchain is a distributed ledger technology with a few desirable security characteristics. The integration of blockchain and MEC can improve the security of current MEC systems and provide greater performance benefits in terms of better decentralization, security, privacy, and service efficiency. Then, the convergence of artificial intelligence (AI) and MEC is presented. A federated learningโempowered MEC architecture is introduced. To improve the performance of the proposed scheme, asynchronous federated learning is proposed. The integration of blockchain and federated learning is also presented to enhance the security and privacy of the federated learningโempowered MEC scheme. Finally, more MEC enabled applications are discussed.
Oct 1, 2021ยท2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Blockchain has the characteristics of decentralization, tamper proof, and untraceability and has been used in various areas. Nowadays, users are increasingly concerned about their privacy. However, anonymity poses a huge challenge on anti-crime, such as anti-money laundering, counter-terrorism financing and tax evasion. In order to achieve a broader use of blockchain, a trade-off between privacy and regulation must be achieved. We study the contradiction between privacy-preserving payment mechanisms including controllable anonymity, and penetrating regulation on digital currency. We then propose a solution based on homomorphic encryption and federated learning. Finally, a digital currency prototype is implemented with corda framework to verify our strategies.
Songqi Wu, Jin Li, Fenghui Duan, Yueming Lu ยท 6 authors
This paper introduces the mainstream secure multi-party computing technology in the blockchain, which provides a reference for further research in the blockchain field. First, we analyze the privacy protection issues in the blockchain and conclude that secure multi-party computing can make up for the security flaws in the blockchain. Secondly, we summarize the developing secure multi-party computing technology based on blockchain, which are the Zero-knowledge proof scheme, the secret sharing scheme, and the homomorphic encryption scheme. At the same time, we have conducted a security analysis of the multi-party computing technology in the blockchain, and believe that these three technologies can well solve the privacy protection problem in the blockchain. Finally, we look forward to the development trend of the secure multi-party computing in the blockchain, which is expected to solve the problems of poor scalability, key distribution, and non-resistance to quantum attacks. Although the secure multi-party computing technology in the blockchain is still in the development stage, the combination of cryptography and blockchain will become the mainstream development trend in the future.
In the process of using smart contracts, users need to provide privacy data such as personal account information to the contract for transactions. However, as the number of users continues to increase, privacy data leakage has become more and more serious. Also, the current model checking methods do not involve verifying the privacy data in smart contracts. For these reasons, we introduce a privacy-preserving framework based on stochastic model checking called VeriPrivData. It formally expresses privacy data by data sensitivity. The smart contract is defined as the Commitments tuple with data sensitivity. Then the Commitments tuple is used to model as DTMC (Discrete Time Markov Chains). We extend PCTL (Probabilistic Computation Tree Logic) to ds-PCTL (Probabilistic Computation Tree Logic with data sensitivity) for describing the privacy requirements. Finally, we verify whether DTMC satisfies the ds-PCTL formula. In this paper, the VeriPrivData framework uses the stochastic model checking tool PRISM, and experiments are carried out. Experimental results show that this method can effectively avoid illegal disclosure of privacy data and enhance the protection of privacy data in the contract.
Since its introduction, Bitcoin has received extensive attentions. With features like privacy and anonymity, Bitcoin has been widely used. However, the mainstream finance and business worlds have not fully embraced Bitcoin yet. This study analyzes the assumption of anonymity and privacy of Bitcoin, give a threat model to Bitcoin privacy. Then present a comprehensive overview on practical attacks on anonymity and privacy of Bitcoin and proposals to improve anonymity of Bitcoin. Compare the state of-the-art proposals that address the privacy threats and enables strong privacy in Bitcoin. Finally, future research directions on privacy of Bitcoin are discussed.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Sajad Meisami, Mohammad Beheshti Atashgah, Mohammad Reza Aref
With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud in healthcare programs, making it difficult for users to have enough control over their data. The recent increment in announced cases of security and surveillance breaches compromising patients' privacy call into question the conventional model, in which third-parties gather and control immense amounts of patients' Healthcare data. In this work, we try to resolve the issues mentioned above by using blockchain technology. We propose a blockchain-based protocol suitable for e-health applications that does not require trust in a third party and provides an efficient privacy-preserving access control mechanism. Transactions in our proposed system, unlike Bitcoin, are not entirely financial, and we do not use conventional methods for consensus operations in blockchain like Proof of Work (PoW). It is not suitable for IoT applications because IoT devices have resources-constraints. Usage of appropriate consensus method helps us to increase network security and efficiency, as well as reducing network cost, i.e., bandwidth and processor usage. Finally, we provide security and privacy analysis of our proposed protocol.
Poonam N. Railkar, Parikshit N. Mahalle, Gitanjali R. Shinde
IoT is a network of interconnected heterogeneous devices which sense, accumulate the data and forward the same to the cloud platform for analytical purposes. There are various IoT verticals in which huge research is going on. IoT security is the most challenging research area in which researchers are investing a huge number of efforts. The challenges in IoT security include access control, trust management, authentication, authorization, privacy, and secured device to device communication. To overcome these, this paper gives an overview of proposed trust based distributed access control approach in IoT. Some of the challenges and threats can be controlled by blockchain technology. Basically, blockchain is an open and distributed ledger of records that can be verified efficiently and stored permanently. This paper checks the feasibility study of the applicability of blockchain in the IoT ecosystem to apply access control mechanism and privacy-preserving policies. This paper discusses how access control and privacy can be addressed by blockchain without compromising security. This paper consists of rigorous gap analysis which is done on the top of comprehensive literature survey. The paper also addresses the challenges and issues which can be faced while applying access control mechanism using blockchain in the context of IoT.
Nur Arifin Akbar, Andi Sunyoto, M. Rudyanto Arief, Wahyu Caesarendra
Today, there is a tendency to reduce the dependence on local computation in favor of cloud computing. However, this inadvertently increases the reliance upon distributed fault-tolerant systems. In a condition that forced to work together, these systems often need to reach an agreement on some state or task, and possibly even in the presence of some misbehaving Byzantine nodes. Although non-trivial, Byzantine Agreement (BA) protocols now exist that are resilient to these types of faults. However, there is still a risk for inconsistencies in the application state in practice, even if a BA protocol is used. A single transient fault may put a node into an illegal state, creating a need for new self-stabilizing BA protocols to recover from illegal states. As self-stabilization often comes with a cost, primarily in the form of communication overhead, a potential lowering of latency - the cost of each message - could significantly impact how fast the protocol behaves overall. Thereby, there is a need for new network protocols such as QUIC, which, among other things, aims to reduce latency. In this paper, we survey current state-of-the-art agreement protocols. Based on previous work, some researchers try to implement pseudocode like QUIC protocol for Ethereum blockchain to have a secure network, resulting in slightly slower performance than the IP-based blockchain. We focus on consensus in the context of blockchain as it has prompted the development and usage of new open-source BA solutions that are related to proof of stake. We also discuss extensions to some of these protocols, speci๏ฌcally the possibility of achieving self-stabilization and the potential integration of the QUIC protocol, such as PoS and PBFT. Finally, further challenges faced in the ๏ฌeld and how they might be overcome are discussed.
In known constructions of classical zero-knowledge protocols for NP, either of zero-knowledge or soundness holds only against computationally bounded adversaries. Indeed, achieving both statistical zero-knowledge and statistical soundness at the same time with classical verifier is impossible for NP unless the polynomial-time hierarchy collapses, and it is also believed to be impossible even with a quantum verifier. In this work, we introduce a novel compromise, which we call the certified everlasting zero-knowledge proof for QMA. It is a computational zero-knowledge proof for QMA, but the verifier issues a classical certificate that shows that the verifier has deleted its quantum information. If the certificate is valid, even unbounded malicious verifier can no longer learn anything beyond the validity of the statement. We construct a certified everlasting zero-knowledge proof for QMA. For the construction, we introduce a new quantum cryptographic primitive, which we call commitment with statistical binding and certified everlasting hiding, where the hiding property becomes statistical once the receiver has issued a valid certificate that shows that the receiver has deleted the committed information. We construct commitment with statistical binding and certified everlasting hiding from quantum encryption with certified deletion by Broadbent and Islam [TCC 2020] (in a black box way), and then combine it with the quantum sigma-protocol for QMA by Broadbent and Grilo [FOCS 2020] to construct the certified everlasting zero-knowledge proof for QMA. Our constructions are secure in the quantum random oracle model. Commitment with statistical binding and certified everlasting hiding itself is of independent interest, and there will be many other useful applications beyond zero-knowledge.
The authentication problem is one of the most significant challenges in the applications of Internet of Things (IoT), and the relationship authentication among smart devices is an effective solution to tackle cross-domain issue. In this article, we first abstract a general undirected graph from the authentication relationship among the smart devices in IoT. Then, we formulate the authentication problem into a signature transitivity problem by incorporating accumulator knowledge and a standard digital signature scheme. As a consequence, the legality of authentication is well-verified by computing the signature and witness of the related edges without worrying about whether the devices belong to the same administrative domain or not. Finally, the efficiency and online time of authority are solved by exploring the blockchain technology, which also leads to the assurance of an online 24-hour third party. The results of analysis and comparison show that the proposed scheme CroDA can well address the practical authentication issue.
Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98$\%$. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.
Ruonan Wang, Min Luo, Yihong Wen, Lianhai Wang ยท 6 authors
There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving data privacy, and supporting trusted AI decision and decentralized AI. Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects. Our analysis of 27 English-language articles published between 2018 and 2021 identifies a number of research challenges and opportunities.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education