Blockchain Papers

Yichen Wan, Youyang Qu, Longxiang Gao, Yong Xiang

No abstract is available for this record.

Haifeng Wang, Junguo Liao

Due to the transparency of the blockchain, the data in the blockchain can be viewed by any joining node, and the privacy is weak. In order to better solve the problem of privacy protection in the current blockchain, for the application of e-commerce blockchain, the Pedersen commitment mechanism is adopted to hide the transaction amount, combined with zero-knowledge proof technology to realize the verification of the transaction amount. At the same time, an efficient range proof scheme based on polynomial commitment and vector inner-product commitment is used to verify whether the transaction amount and the balance of both parties are greater than zero. Finally, use the homomorphism promised by Pedersen to update the ciphertext ledger. The security analysis and efficiency test of the proposed blockchain privacy protection algorithm show that compared with the existing schemes, the proposed algorithm has the advantages of strong security and higher efficiency.

Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs · 5 authors

No abstract is available for this record.

Owen Cutajar, Naghmeh Moradpoor, Zakwan Jaroucheh

In a perfect world, coordination and cooperation across distributed autonomous systems would be a trivial task. However, incomplete information, malicious actors and real-world conditions can provide challenges which bring the trust-worthiness of participants into question. In this work-in-progress paper, we propose the novel use of Distributed Ledger Technologies with a particular focus on IOTA to provide a trust overlay governing information interchange across autonomous vehicles. This paper outlines a number of scenarios where information interchange could provide useful data for decision making, defines how trust can be useful in this context, and provides use cases which call for evaluating the trustworthiness of the message. The paper also outlines the architecture of an IOTA-based private tangle integrated with vehicle simulation software which facilitates the evaluation of various scenarios.

Srini Bhagavan, Mohamed Gharibi, Praveen Rao

With increased awareness comes unprecedented expectations. We live in a digital, cloud era wherein the underlying information architectures are expected to be elastic, secure, resilient, and handle petabyte scaling. The expectation of epic proportions from the next generation of the data frameworks is to not only do all of the above but also build it on a foundation of trust and explainability across multi-organization business networks. From cloud providers to automobile industries or even vaccine manufacturers, components are often sourced by a complex, not full digitized thread of disjoint suppliers. Building Machine Learning and AI-based order fulfillment and predictive models, remediating issues, is a challenge for multi-organization supply chain automation. We posit that Federated Learning in conjunction with blockchain and smart contracts are technologies primed to tackle data privacy and centralization challenges. In this paper, motivated by challenges in the industry, we propose a decentralized distributed system in conjunction with a recommendation system model (Matrix Factorization) that is trained using Federated Learning on an Ethereum blockchain network. We leverage smart contracts that allow decentralized serverless aggregation to update local-ized items vectors. Furthermore, we utilize Homomorphic Encryption (HE) to allow sharing the encrypted gradients over the network while maintaining their privacy. Based on our results, we argue that training a model over a serverless Blockchain network using smart contracts will provide the same accuracy as in a centralized model while maintaining our serverless model privacy and reducing the overhead communication to a central server. Finally, we assert such a system that provides transparency, audit-ready and deep insights into supply chain operations for enterprise cloud customers resulting in cost savings and higher Quality of Service (QoS).

Yuye Han, Hong Chen, Zhijie Qiu, Lei Luo · 5 authors

As an essential means of privacy protection technology, zero-knowledge proof has gradually been applied into various fields with the development of blockchain technology, such as the Internet of Vehicles and Bitcoin. Personal comprehensive credit score as a measure to promote social governance is closely related to personal privacy. Although there are currently credit score calculation systems for various application scenarios, these systems almost ignore user privacy protection, which leads to user information leakage or abuse. The combination of zero-knowledge proof and personal credit score calculation has been studied by a number of researchers at the present stage. However, all data are provided by users directly in the current schemes, which did not consider the data authenticity under the situation that users provided false data. In this paper, we utilize zero-knowledge proof to design a novel privacy protection scheme for personal credit score calculation, taking into account the authenticity verification of multi-dimensional user data. In addition, our scheme also proposes the concept of a universal verification platform based on blockchain for personal credit scores. This platform has more substantial applicability and versatility for any qualified institution that requires querying and verifying user’s credit scores. At the end of the paper, we conducted a security analysis and performance evaluation for the overall scheme.

Hisham Ali, Pavlos Papadopoulos, Jawad Ahmad, Nikolaos Pitropakis · 6 authors

Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the risk of a potential cyberattack - the requirements for threat information sharing range from simplistic sharing of documents to threat intelligence sharing. Therefore, the storage and sharing of highly sensitive threat information raises considerable concerns regarding constructing a secure, trusted threat information exchange infrastructure. Establishing a trusted ecosystem for threat sharing will promote the validity, security, anonymity, scalability, latency efficiency, and traceability of the stored information that protects it from unauthorized disclosure. This paper proposes a system that ensures the security principles mentioned above by utilizing a distributed ledger technology that provides secure decentralized operations through smart contracts and provides a privacy-preserving ecosystem for threat information storage and sharing regarding the MITRE ATT\&CK framework.

Sankarshan Damle, Boi Faltings, Sujit Gujar

AI applications find widespread use in a variety of domains. For further acceptance, mostly when multiple agents interact with the system, we must aim to preserve the privacy of participants information in such applications. Towards this, the Yao’s Millionaires’ problem (YMP), i.e., to determine the richer among two millionaires’ privately, finds relevance. This work presents a novel, practical, and verifiable solution to YMP, namely, Secure Comparison Protocol (SCP). We show that SCP achieves this comparison in a constant number of rounds, without using encryption and not requiring the participants’ continuous involvement. SCP uses semi-trusted third parties - which we refer to as privacy accountants - for the comparison, who do not learn any information about the values. That is, the probability of information leak is negligible in the problem size. In SCP, we also leverage the Ethereum network for pseudo-anonymous communication, unlike computationally expensive secure channels such as Tor. We present a Secure Truthful cOmbinatorial aUction Protocol (STOUP) for single-minded bidders to demonstrate SCP’s significance. We show that STOUP, unlike previous works, preserves the privacies relevant to an auction even from the auctioneer. We demonstrate the practicality of STOUP through simulations.

Ying He, Ke Huang, Guangzheng Zhang, F. Richard Yu · 6 authors

Machine learning (ML) algorithms are essential components in autonomous driving. In most existing connected and autonomous vehicles (CAVs), a large amount of driving data collected from multiple vehicles are sent to a central server for unified training. However, data privacy and security have become crucial during the data-sharing process. Federated learning (FL) for data security has arisen nowadays, and it can improve the data privacy of distribute machine learning. However, the malicious attackers can still be able to attack the training process. Due to the complete reliance on the central server, FL is very fragile. To address the above problem, we propose Bift: 1) a fully decentralized ML system combined with FL and 2) blockchain to provide a privacy-preserving ML process for CAVs. Bift enables distributed CAVs to train ML models locally using their own driving data and then to upload the local models to get a better global model. More importantly, Bift provides a consensus algorithm named Proof of Federated Learning to resist possible adversaries. We evaluate the performance of Bift and demonstrate that Bift is scalable and robust, and can defend against malicious attacks.

Rakib Ul Haque, A S M Touhidul Hasan, Tasnia Nishat, Md. Akhtaruzzaman Adnan

No abstract is available for this record.

Yanru Chen, Jingpeng Li, Fan Wang, Kaifeng Yue · 8 authors

With the development of big data and blockchain, an increasing number of scholars have begun to study blockchain for data sharing. By studying data sharing models that are based on blockchain, we find that almost all of them have the following problems: 1) it is difficult to protect the privacy and integrity of users’ data, along with users’ data ownership; 2) the storage burden of blockchain is heavy, and blockchain lacks a mechanism for dealing with data with diverse types and inconsistent formats; and 3) the consensus mechanism has low fairness or low efficiency. Therefore, we propose a data-sharing privacy protection model (DS2PM) that is based on blockchain and a federated learning mechanism for solving these problems. The safety analysis and experimental results show that the DS2PM outperforms the previously established schemes.

Julia Rosenberger, Felix Rauterberg, Dieter Schramm

Due to the big advantages like immutable, decentralized data record and smart contracts (SC), distributed ledger technologies (DLT) gain strongly in importance in a wide variety of areas. With the fourth industrial revolution and the industrial internet of things (IIoT), new business models and technology fields based on data-driven approaches evolve and lead to the demand for trustworthy data and secure data exchange in a decentralized system. While a comprehensive number of possible industrial use cases exists, there is a lack of experimental studies on their applicability on IIoT devices. Available performance tests mostly focus on the performance of the DLT but not on their influence on the resources of the IIoT device. Furthermore, one very important DLT, namely IOTA, which is explicitly designed for application in IoT environments, did not receive much attention yet. This was due to two major drawbacks, namely the need for a centralized instance and the lack of SC functionality in the first IOTA version compared to other DLTs such as Hyperledger. The recently published version IOTA Coordicide improved in both. This paper presents detailed results from two industrial use cases and experiments on a private DLT network based on IOTA in IIoT, focusing on the resource demands for the IIoT devices with different network setups. The results confirm the suitability of IOTA for IIoT devices. Furthermore, an overview of the required resources of the IIoT devices with different transaction rates and networks sizes is given.

Saurabh Singh, Shailendra Rathore, Osama Alfarraj, Amr Tolba · 5 authors

No abstract is available for this record.

Qingchen Wang, Yuan Chen, Tang Hongyu

Radio frequency identification technology (RFID) is widely used due to its advantages of contactless identification. How to ensure the location privacy of tags and their users has become an urgent problem in the development of RFID technology. In order to resist active attacks such as tampering and counterfeiting caused by rewriting encryption and canceling encryption by malicious attackers. According to the existing location privacy protection schemes, this paper uses non-interactive zero-knowledge proof (NIZK) and universal re-encryption, then designs a publicly verifiable location privacy re-encryption scheme. In this scheme, all calculations are performed by the anonymizer. The anonymizer not only performs the ciphertext re-randomization operation, but also needs to provide a NIZK for this. The next anonymizer must verify the proof before re-randomization. The tag only needs to provide a certain reading and writing function to ensure that the ciphertext re-randomized each time will not be tampered with. We can see that this scheme can well protect the tag and user's location privacy through analysis.

Fei Song, Letian Li, Yikun, Li - · 7 authors

Emerging information technologies have accelerated the construction of the Industrial Internet of Things (IIoT). Unfortunately, the explosive growth of various services has caused serious challenges to access control in terms of scalability and trustworthiness. In this paper, we proposed the smart collaborative contract based endogenous access control mechanism. First, access control procedures running on centralized servers are migrated to distributed equipment located inside the data plane, which can reduce the transmission overhead and improve service capacity. Second, the smart collaborative contract is designed for credibility enhancement since part of attackers may attempt to compromise access control. It covers terminal behavior assessment and service request recording. Experiments conducted in a prototype show that the performance of the proposed scheme is better than the traditional solution and is resistant to common attacks.

Xiaohui Guo

In recent years, Artificial intelligence (AI) have been applied in many fields, including driverless cars, smart cities, healthcare, finance, etc. However, data island and data privacy security are still two major challenges for AI, federated learning is proposed as a solution. In federated learning, many clients collaborate to train a common model under the coordination of a central server, while keeping the training data decentralized. Each client's data does not leave the local area, and a global shared model is jointly built by means of parameter exchange under the encryption mechanism, the built model serves only the local target in their respective regions. In this paper, we present the definition, classification, and learning process of the federated learning, and discuss the key challenges faced by federated learning and the solutions that are currently available.

Lu Ding, Yong Zhao

Selfish mining has potential hazards to blockchain systems by hiding mined blocks and broadcasting them strategically. It lets the adversary gain additional rewards in the mining process which was first proposed in Bitcoin recommended by Satoshi Nakamoto. The blockchain-based application Ethereum using GHOST (Greedy Heaviest-Observed Sub-Tree) protocol with regard to Bitcoin to alleviate the loss of honest miners of stale blocks and compensate uncle blocks which will increase if selfish mining occurs. But it only makes things worse since uncle incentive mechanism reduces the cost of the failure of selfish mining and decreases the threshold of selfish mining pool needed to be profitable. There is no research which focuses on the rationale behind uncle incentive mechanism in Ethereum. This paper focuses on the feasible modifications on uncle incentive mechanism against selfish mining in Ethereum. The uncle block reference behaviors of miners are analyzed when selfish mining occurs in Ethereum by building models. Furthermore, a feasible uncle incentive mechanism against selfish mining is proposed. It is not necessary to have a strategy with a monotonous order with regard to generations of uncle blocks. The uncle incentive mechanism suggested in this paper can efficiently raise the threshold for selfish mining to be profitable by around 3.17%, an increase by around 9.76%. It offers an alternative uncle incentive mechanism for cryptocurrencies which are based on GHOST protocol.

Feng Xiong, Cheng Xu, Wei Ren, Rongyue Zheng · 6 authors

No abstract is available for this record.

Aofan Liu, Mst. Surma Khatun, Hanting Liu, Mahdi H. Miraz

Both the internet of things (IoT) and distributed ledger technology (DLT), more commonly known as the blockchain, are two popular emerging technologies of this era. While blockchain offers strengthened security, along with other benefits, it requires peer-to-peer (P2P) nodes for its consensus process. On the contrary, IoT ecosystems inherently consist of many P2P nodes but it is highly critiqued for its lack of security measures. Therefore, the fusion of these complementary duos, known as the blockchain of things (BCoT), has become a recent research trend. While the fit is good and the benefits such consolidation can offer are obvious, a lot of challenges are yet to be addressed. Therefore, we have conducted a comprehensive literature review, covering 33 research articles, spanning over the last six years (2016–2021), to report the state-of-the-art research in this domain. We have synthesised the existing literature by comparing, contrasting, resembling as well as critically evaluating them and thus, deduced the current challenges and future research directions, particularly with regards to lightweightness.

Mohamed Anass Amallah, Noreddine Abghour, Khalid Moussaid, Amina El Omri · 5 authors

No abstract is available for this record.

Qianyu Wang, Shaowen Qin

A blockchain is an ever-growing list of records that are linked to each other in a distributed network. These linked records called ledgers are immutable in nature providing resistance to change. Blockchain provides a secure way of processing the data in a distributed environment. It was widely involved in crypto currencies in the earlier days and however its application in bit coin motivated and inspired other applications to adapt its concepts. Its application in healthcare requires blockchain to be highly secure, provide a more trusted environment than the traditional blockchain, that is by design should be an enterprise level blockchain by restricting access to the public. Hyperledger Fabric caters to all these requirements in providing a secure and distributed environment for healthcare systems. In healthcare there are a lot of fields where Hyperledger Fabric can be adopted, but the focus here is given to management of patient's medical records. Traditionally the medical records are either stored centrally in a database that is accessible to only the hospitals owning it, this creates a several of problems for patients. The aim is to consider the records are handled, how the patient will interact in the real world and design a system using hyper ledger Fabric to tackle major problems using smart contract.

Ke Yuan, Yingjie Yan, Tong Xiao, Wenchao Zhang · 6 authors

In response to the rapid growth of credit-investigation data, data redundancy among credit-investigation agencies, privacy leakages of credit-investigation data subjects, and data security risks have been reported. This study proposes a privacy-protection scheme for a credit-investigation system based on blockchain technology, which realizes the secure sharing of credit-investigation data among multiple entities such as credit-investigation users, credit-investigation agencies, and cloud service providers. This scheme is based on blockchain technology to solve the problem of islanding of credit-investigation data and is based on zero-knowledge-proof technology, which works by submitting a proof to the smart contract to achieve anonymous identity authentication, ensuring that the identity privacy of credit-investigation users is not disclosed; this scheme is also based on searchable-symmetric-encryption technology to realize the retrieval of the ciphertext of the credit-investigation data. A security analysis showed that this scheme guarantees the confidentiality, the availability, the tamper-proofability, and the ciphertext searchability of credit-investigation data, as well as the fairness and anonymity of identity authentication in the credit-investigation data query. An efficiency analysis showed that, compared with similar identity-authentication schemes, the proof key of this scheme is smaller, and the verification time is shorter. Compared with similar ciphertext-retrieval schemes, the time for this scheme to generate indexes and trapdoors and return search results is significantly shorter.

Jing Wu, Pan Zhou, Qimei Chen, Zichuan Xu · 6 authors

With the rapid growth of Internet of Things (IoT), smart home develops rapidly in these years, which could assist people who need family medical support. It could integrate health care with ambient assisted living (AAL) technologies and provide activities of daily life (ADLs) to the people who need care. This paper proposes a smart home and cross-cloud-and-edge computing based nursing system (NS). In general, a good NS requires low latency, high stability, and the real-time analysis and response, where the conventional centralized cloud computing based approaches cannot meet those requirements very well. To this end, we introduce a novel distributed joint edge-cloud structure to better satisfy these requirements. Moreover, to deal with the security and privacy issues, we introduce the blockchain to verify the identity of data exchanging and differential-privacy (DP) in the NS to protect the healthcare takers’ data privacy. In a word, we propose a privacy-preserving context-aware multi-armed bandit based online learning approach for edge-cloud-enabled NS via blockchain in IoTs. Additionally, our system with a novel top-down expanding tree based structure can support dynamically increasing health care datasets. Extensive experimental and numerical results demonstrate our solution can achieve accurate recommendation results with sublinear regret performance.

Yufeng Li, Yuling Chen, Tao Li, Xiaojun Ren

In the blockchain-based energy transaction scenario, the decentralization and transparency of the ledger will cause the users’ transaction details to be disclosed to all participants. Attackers can use data mining algorithms to obtain and analyze users’ private data, which will lead to the disclosure of transaction information. Simultaneously, it is also necessary for regulatory authorities to implement effective supervision of private data. Therefore, we propose a supervisable energy transaction data privacy protection scheme, which aims to trade off the supervision of energy transaction data by the supervisory authority and the privacy protection of transaction data. First, the concealment of the transaction amount is realized by Pedersen commitment and Bulletproof range proof. Next, the combination of ElGamal encryption and zero-knowledge proof technology ensures the authenticity of audit tickets, which allows regulators to achieve reliable supervision of the transaction privacy data without opening the commitment. Finally, the multibase decomposition method is used to improve the decryption efficiency of the supervisor. Experiments and security analysis show that the scheme can well satisfy transaction privacy and auditability.