Blockchain Papers

Mayank Swarnkar, Robin Singh Bhadoria, N.G. Sharma

No abstract is available for this record.

R. Varsha, Meghna Manoj Nair, Siddharth M. Nair, Amit Kumar Tyagi

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

Soeren Bittins, Gerhard Kober, Andrea Margheri, Massimiliano Masi · 6 authors

No abstract is available for this record.

Qinan Li, Zhihao Xue

With the rapid development of blockchain, many service providers tend to provide users with network resources and services on the basis of blockchain, and most network resources are inseparable from the authorization. However, the current authorization mechanism based on blockchain is not perfect, which is easy to cause the user's identity attributes to be leaked, and not conducive to protecting the privacy of users. In order to improve the authorization mechanism based on the blockchain and protect the user's privacy to the greatest extent during the authorization process, we proposed a privacy-protecting authorization system based on blockchain and zk-SNARK. First of all, SP can authorize users through the blockchain smart contract. Second, in the authorization process, we introduced zk-SNARK. Using the zero-knowledge property of zk-SNARK, the SP can still authorize the users without knowing the identity attribute value of the users. Therefore, the proposed system can better protect the users' attribute privacy. Finally, we analyze the security of the proposed system, and the results show that it is computationally infeasible for an attacker to attack the system.

Avani J. Dadhania, Hiren Patel

As the number and variety of connected objects continue to grow and the devices themselves become smarter, IoT has demonstrated to be the foremost rising innovation where millions of gadgets can interface to the web which makes the life attainable with no human intercession. However, the security and privacy of this IoT environment face some issues that must be overcome to meet the novel solution for IoT applications with no margin for error. Hence, secure architecture for centralized servers is necessitating maintaining the authenticity and protection of information. However, centralized architecture suffers from some challenges viz. reduction in performance, single point of failure and prone to attacks which could be addressed through decentralized or distributed architecture. Blockchain, being decentralized in nature, immutable in function, transparent in-process and reliable without a trusted third party, could be efficient attention for addressing IoT security challenges. In this research work the security concerns like access control mechanisms for IoT applications like smart city, smart healthcare system through Blockchain innovation is addressed. Blockchain being a distributed ledger provides an approach to securely store the data cryptographically. Specifically, proposed work intend to cover different access control methods for the protection of information and IoT security. This paper makes an exhaustive review of how decentralized architecture using Blockchain might potentially improve IoT access control.

Xiaodong Ren, Feilong Lin, Zhongyu Chen, Changbing Tang · 6 authors

The abuse of personal identity information is one of the most serious problems worldwide. Most social services or businesses use the identity authorization to confirm their validity and legality and the copies of users' identity certification are usually recorded by the service providers. It is easy to leak the users' identity information due to the untrustworthy service provider or single-point security failure, and various social problems are then caused. To deal with such problems, this paper proposes a Blockchain-based Identity Authorization mechanism (BIA). First, an Identity Authorization Module (IAM) is devised, which reads the identity certificate and transform the identity plaintext to ciphertext under the authorization by the user's identity certificate entity and password. IAM guarantees the security of identity information by keeping its plaintext offline. Second, a Business Contract Module (BCM) is designed, which provides a general smart contract framework for identity authorization that can be adopted by most of social services or businesses. Third, a double-chain blockchain infrastructure is developed, whereby the encrypted identity information and service smart contracts are respectively recorded in the tamper-resistant, non-repudiable, and publicly verifiable way. Finally, a prototype system has been developed to verify the security, feasibility and effectiveness of the proposed BIA.

Vidushi Agarwal, Sujata Pal

Internet of Things, delineated as a network of connected heterogeneous devices is emerging as a widely adopted technology in almost all walks of life today. The massive increase in the number of IoT devices has introduced several issues related to security and management. Blockchains can be a promising technology to make IoT systems secure and distributed for the time to come. However, current blockchain systems are not capable of scaling in accordance to the huge IoT data without a loss in speed and time efficiency. Therefore, we use the concept of sidechains and offline data storage to alleviate the scalability issue of blockchains. In this work, we propose an architectural framework for the security and maintenance of IoT systems using blockchain technology. Smart contracts are used to enforce data authentication, authorization, and keep track of all the activities. Extensive simulation and analysis results demonstrate that the proposed blockchain architecture is highly scalable (in terms of average latency, throughput and cost) and can be applied efficiently in the IoT system.

Yuwei Sun, Hiroshi Esaki, Hideya Ochiai

With the approach of 5G Society, more and more devices have been connected to the Internet, where information is stored, analyzed, and shared. Federated learning allows participants to train a machine learning model through sharing the parameters of it based on local training, instead of raw private data at local. In this research, we propose the implementation of the blockchain in federated learning for local parameters evaluation and global parameter aggregation, thus alleviating the influence of end-point adversarial training data. Besides, all updates of local parameters are encrypted and stored in a block of the blockchain after the consensus by the committee. We evaluate the performance of the scheme when adopting various types of corruption to the adversary's dataset, including noise with various degrees and circle occlusion with various diameters. At last, it shows robust and resilient performance compared with the traditional federated learning, achieving a validation accuracy rate of 0.957 when adding noise with a degree of 1.0, and one of 0.944 when adopting circle occlusion with a diameter of 28 pixels for the classification.

Yan Ding, Liaoliao Feng, Ying Qin, Chenlin Huang · 7 authors

Sharing data assets has emerged among organizations with variable interest, especially in the manner of federation. Blockchain has been used in data sharing systems to provide the audit and access control on data because of its tamper-proof characteristics. However, the user's authority is difficult to accurately describe because of the complexity of users' multidimensional roles in the federation. And when developing the decentralized access control mechanism with the support of blockchain techniques, how to improve the efficiency of the decision for access control is the problem that is necessary to be solved. In this paper, we propose a blockchain-based multidimensional user authorization and role-based access control mechanism in the federated data sharing system. The colored coin is defined to identify the user's authority in multidimension, and decentralized access control decision is achieved through the automatic execution of smart contracts, thereby effectively enhancing the mechanism robustness and improving the security decision performance. The system is implemented on the basis of HyperLedger Fabric. Experimental results show that the proposed scheme is feasible and efficient. The decision efficiency is remained stable with the increasing of policy rules.

Chao Qiu, Xiaofei Wang, Haipeng Yao, Zehui Xiong · 6 authors

The revolutions of computing and communication have opened up demands for the high quality of service (QoS), such as high data transmission, high reliability, and low latency. These new opportunities have spawned numerous studies on edge computing and artificial intelligence (AI), even the cooperation between them, referred to as edge intelligence. However, there are a number of handicaps that prevent edge intelligence from being used as a generic platform. The most intractable one is the heterogeneity and un-credibility among edges, hindering the way of sharing the learning results reliably, flexibly, and efficiently. In this paper, we propose a blockchain-assisted edge intelligence (B-EI) approach to solve the problem. The edge learning nodes train their local intelligence, followed by the improved blockchain to share the local intelligence, constructing edge intelligence among the heterogeneous and uncredible edges. Specifically, the improved blockchain employs a novel learning-measured consensus protocol, named Proof of Learning. The edges, also acted as the blockchain nodes, compete to have more superior local intelligence, instead of solving a hashed result. The superior local intelligence is then shared and distributed with other edges. It is not only beneficial to achieve edge intelligence, but also efficient to employ the computation resource, by replacing the hashing as the intelligence training. In order to show the potential benefits, we then use the proposed B-EI approach to solve a joint resource assignment problem. Simulation results show that our scheme outperforms the other state-of-art solutions, in terms of training episodes, and resource utility.

Jingwei Liu, Tianyu Liang, Rong Sun, Xiaojiang Du · 5 authors

Electronic medical records (EMRs) can provide medical institutes with a number of research materials, which could promote the development of medical field. In order to protect patients' privacy while achieving efficient medical data sharing, we propose a privacy-preserving medical data sharing scheme based on consortium blockchain. In the scheme, a conditional anonymous tracing mechanism is designed to protect users' privacy, in which malicious users can be tracked when illegal behavior occurs. However, as the number of users grows, masses of medical data might touch the storage bottleneck of blockchain. Therefore, an on-chain-off-chain storage model is designed to alleviate the storage burden of blockchain, in which only the metadata of EMRs are recorded in blockchain while the patients' EMRs are stored in cloud after being encrypted. In order to resist the collusion attack, an improved proxy re-encryption scheme is introduced to ensure that EMRs can only be accessed by the authorized data requester. Moreover, security analysis and simulation demonstrate that the proposed scheme is more secure and efficient.

Dawei Wang, Xiao Chen, Haiqin Wu, Ruozhou Yu · 5 authors

Vehicular crowdsourcing networks (VCNs) enable vehicles to provide or obtain traffic-related services in a costefficient and flexible manner. Therefore, it is crucial to provide trusted management in VCNs for high reliability towards both service producers and consumers. However, most recent VCN platforms rely on a third party to manage crowdsourcing services which might be not fully trusted by users. For the issue, this paper proposes a blockchain-based trust management scheme for VCNs to provide a decentralized and trusted service management. A comprehensive trust evaluation model (TEM) is designed to quantify the trust degree of each vehicular node, and a vehicle-trust blockchain framework called VTchain is proposed to preserve the trust values of nodes while guaranteeing transparency and trustworthiness. Particularly, we leverage a trusted execution environment (TEE) to provide secure trust evaluation to tackle possible untrusted road-side units. In addition, we introduce TEM-based Proof of Trust to support blockchain maintenance, which works together with an efficient consensus algorithm Zyzzyva for improved scalability. Finally, extensive experiments are conducted by developing a testbed deployed on cloud servers for measurements.

Zhiyu Xu, Lin Yang, Ziyuan Wang, Sheng Wen · 7 authors

Blockchain brings opportunities and challenges for financial data sharing services. The essential properties in a distributed multi-parties system are data access control and privacy control. This paper proposes a hierarchical data access model for financial services, which contains consent management and dynamic credits management. We implement fine-grained data access control through rating accredited data recipients (ADRs). By accessing corresponding blockchain service logs, ADRs' credits will dynamically be updated. The credits evaluation algorithm is responsible for calculating ADRs' credits based on their completion rate, business ethics rate, and feedback positive rate. Moreover, through applying smart contracts, the efficiency of consent management can be improved, and privacy policies can be managed elastically. Finally, we deploy smart contracts on the Ethereum Rinkeby testnet to evaluate the model feasibility. Furthermore, the theoretical analysis and experimental results indicate that the prototype is secure and efficient.

Shuhui Fan, Haoran Xu, Shaojing Fu, Ming Xu

Decentralized applications (DApp) have opened up new heights for the use of blockchain and cryptocurrencies, but users with poor intentions utilize DApp websites to promote smart Ponzi schemes, causing huge losses to inexperienced investors. To alert potential investors, DApp websites tend to mark high-risk DApps uploaded by users. So far, previous works have only trained the classifiers of smart Ponzi schemes in a unilateral way. However, smart contract dataset is very skewed, there are much fewer smart Ponzi schemes than non-Ponzi schemes. Besides, due to data security and privacy, different DApp websites are generally not allowed to share their users' data and information. As a result, it is difficult for a certain DApp website to learn the patterns of smart Ponzi schemes and detect them alone.This paper proposes a novel smart Ponzi scheme detection framework named SPSD-FL (Smart Ponzi Scheme Detection in Federated Learning) for the first time to address these problems. Instead of unilateral training, our method fulfills a secure aggregation of local gradient histograms by a horizontal federated learning based on XGBoost algorithm, in which we leverage a data augmentation method and labeled smart Ponzi schemes' code features, dapp submitters' information, and investors' information to implement the collaborative training of the detection model between different DApp websites without sharing the original training data and protecting the user's sensitive information. Experiments conducted on the real dataset show that our method can be trained substantially more data-efficiently and the SPSD-FL model achieves an average F-score of 96.55%, which is better than the unilateral training model and no less than that of the centralized training model.

Chen Zeng, Yifan Wang, Fan Liang, Xiaohui Peng

Superior to traditional vehicles, intelligent vehicles (IV) can share data in Vehicular Ad-Hoc Networks (VANETs) to provide a more comfortable and safer driving experience, based on the assumption that the data sharing is accountable and reliable. However, trusted data sharing in VANETs is always a paramount concern. We define that the trusted data sharing includes the three properties, namely data sharing accountability, privacy preservation, and transmission confidentiality. To address the problem, we propose a comprehensive solution including the trusted ledger model (TLM) and implement the Fengyi system to verify it. The TLM is a model that ensures the consistency of multiple data resources in a low trust distributed computing environment. Then, distributed Fengyi ledgers based on the TLM are proposed to keep data sharing accountable and private in VANETs. The Fengyi system is designed and implemented to provide authentication and encrypted communication services with the ledgers. Finally, we deploy the Fengyi system on three different platforms, checking the effectiveness and efficiency of the Fengyi system. The results show that the system can ensure trusted data sharing in VANETs, and the time cost for the verification of data sharing on-road is average 253.33μs, 38% lower than that in recent research.

Ahmad Alsharif, Mahmoud Nabil

The unprecedented growth of decentralized technologies and the abundance of healthcare data creates numerous opportunities for the digital healthcare industry and poses major challenges for data security. In this paper, we propose a novel decentralized blockchain-based medical data marketplace in which medical record sellers can sell their data to interested buyers, e.g., pharmaceutical corporations. Sellers use a smart contract to exchange their records with buyers for a digital currency. In our model, sellers can enforce flexible access control policy on the encrypted records while allowing the buyers to verify the correctness of the encrypted records without revealing any information about the records using the developed zk-SNARK protocol. In addition, sellers acquire a proof-of-delivery to redeem buyers' contingent payments by exchanging a record access key for a buyer signature using the developed trustless zero-knowledge contingent payment protocol. Our security analysis proves that our model is secure against malicious behaviors of both dishonest sellers/buyers. Performance evaluation indicates that the GAS cost on Etherume blockchain and the computational cost of the cryptographic operations are low.

Meng Li, Jianbo Gao, Yifei Chen, Jingcheng Zhao · 5 authors

Ride-hailing is a favored vehicular service model where drivers can deliver convenient rides to waiting riders via responding to a road-side unit or a ride-hailing service provider. However, previous works did not consider the order-linking function where a rider Cathy waving for a ride will be matched to a driver Bob in service with rider Alice whose destination is close to the start point of Cathy. Furthermore, a malicious matching executor could collude with an appointed driver to interfere with the matching process, which causes service unfairness and has not been addressed before. To mitigate these limitations, we first propose a privacy-preserving ride-hailing scheme OLink with the verifiable order-linking property. Specifically, we adopt road network partitioning and range query to achieve basic user matching. The user matching process supports range conditions and protects users' privacy. Next, a Proof-of-Linking protocol is designed based on the zero-knowledge succinct non-interactive argument of knowledge, zero-knowledge proof, and Bloom filters to enable the driver in service to generate three consecutive proofs for linking a current order to the next rider's order in advance; the proofs will be released such that anyone can verify the proofs and matching fairness is guaranteed. Finally, we formally prove the privacy and security of OLink, and then evaluate its performance with PySNARK to demonstrate feasibility and efficiency.

Rahma Mukta, James Martens, Hye-Young Paik, Qinghua Lu · 5 authors

Sharing credentials could raise privacy concerns. For digital credentials to be widely accepted, there is a need for an end-to-end system that provides (i) secure verification of the participant identities and credentials to increase trust, and (ii) a data minimisation mechanism to reduce the risk of oversharing the credential data. This paper proposes CredChain, a blockchain-based Self-Sovereign Identity (SSI) platform architecture that allows secure creation, sharing and verification of credentials. Beyond the verification of identities and credentials, a flexible selective disclosure solution is proposed using redactable signatures. The credentials are managed through a decentralised application/wallet which allows users to store their credential data privately under their full control and re-use as necessary. Our evaluation results show that CredChain architecture is feasible, secure and exhibits the level of performance that is within the expected benchmarks of the well-known blockchain platform, Parity Ethereum.

Safa Otoum, Ismaeel Al Ridhawi, Hussein T. Mouftah

The advances in today's IoT devices and machine learning methods have given rise to the concept of Federated Learning. Through such a technique, a plethora of network devices collaboratively train and update a mutual machine learning model while protecting their individual data-sets. Federated learning proves its effectiveness in tackling communication efficiency and privacy-safeguarding issues. Moreover, blockchain was introduced to solve many network issues in regard to data privacy and network single point of failure. In this article, we introduce a solution that integrates both federated learning and blockchain to ensure both data privacy and network security. We present a framework to decentralize the mutual machine learning models on end-devices. A blockchain-based consensus solution as a second line of privacy is used to ensure trustworthy shared training on the fog. The proposed model enables on-end device machine learning without any centralized training of the data nor coordination by utilizing a consensus method in the blockchain. We evaluate and verify our proposed model through simulation to showcase the effectiveness of the adapted scheme in terms of accuracy, energy consumption, and lifetime rate, along with throughput and latency metrics. The proposed model performs with an accuracy rate of ≈ 0.97.

Eleftherios Kokoris-Kogias, Enis Ceyhun, Linus Gasser, Philipp Jovanovic · 6 authors

Distributed ledgers provide high availability and integrity , making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality , however. This work enhances permissioned and permissionless blockchains with the ability to manage confidential data without forfeiting availability or decentralization. The proposed Calypso architecture addresses two orthogonal challenges confronting modern distributed ledgers: (a) enabling the auditable management of secrets and (b) protecting distributed computations against arbitrage attacks when their results depend on the ordering and secrecy of inputs. Calypso introduces on-chain secrets, a novel abstraction that enforces atomic deposition of an auditable trace whenever users access confidential data. Calypso provides user-controlled consent management that ensures revocation atomicity and accountable anonymity. To enable permissionless deployment, we introduce an incentive scheme and provide users with the option to select their preferred trustees. We evaluated our Calypso prototype with a confidential document-sharing application and a decentralized lottery. Our benchmarks show that transaction-processing latency increases linearly in terms of security (number of trustees) and is in the range of 0.2 to 8 seconds for 16 to 128 trustees.

Xudong Li, Farooq Ahmed, Lingbo Wei, Chi Zhang · 5 authors

The last decade has witnessed fast development of blockchain techniques. However, the high cost of storage space and network bandwidth caused by data synchronization prevents many nodes from joining the network, and becomes a bottleneck impeding the development of blockchain. Traditional schemes typically attempt to transfer most of the storage and computation tasks from a light client to a full node. Nevertheless, they remain susceptible to privacy attacks because light clients need to query and retrieve blockchain data. In this paper, we first describe the privacy issues and challenges for Ethereum data retrieval and then propose a privacy-preserving scheme based on private information retrieval (PIR) to secure retrieval of blockchain data. The main idea is to achieve pointer based PIR search by keywords and introduce differential privacy to mitigate PIR's performance barrier. Hence we achieve a tradeoff between privacy and performance. The evaluations on the Ethereum dataset and analysis show that our scheme is both effective and practical in protecting blockchain access privacy.

Amjad Qashlan, Priyadarsi Nanda, Xiangjian He

There has been wide range of applications involving smart home systems for user comfort and accessibility to essential commodities. Users enjoy featured home services supported by the IoT smart devices. These IoT devices are resource-constrained, incapable of securing themselves and can be easily hacked. Edge computing can provide localized computations and storage which can augment such capacity limitations for IoT devices. Furthermore, blockchain has emerged as technology with capabilities to provide secure access and authentication for IoT devices in decentralized manner. In this paper, we propose an authentication scheme which integrate attribute based access control using smart contracts with ERC-20 Token (Ethereum Request For Comments) and edge computing to construct a secure framework for IoT devices in Smart home system. The edge server provide scalability to the system by offloading heavier computation tasks to edge servers. We present system architecture and design and discuss various aspects related to testing and implementation of the smart contracts. We show that our proposed scheme is secure by thoroughly analysing its security goals with respect to confidentiality, integrity and availability. Finally, we conduct a performance evaluation to demonstrate the feasibility and efficiency of the proposed scheme.

Qi Wang, Ming Xu, Xiangxue Li, Haifeng Qian

There are many disparate conceptualizations to secure a cryptocurrency network. Bitcoin relies on the proof-of-work mechanism and heterogeneous altcoins use proof-of-stake. Delegated Proof-of-Stake (DPoS), known to be a fast, efficient, decentralized, and highly flexible blockchain design, further offers some interesting reshaping that are well worth deliberating. In DPoS consensus, a panel of trusted parties (called committee of producers in the paper) has to be established, with all of its members eligible to create blocks and prevent non-trusted parties from participating. Deterministic selection of block producers allows transactions to be confirmed in an expected average of 1 second. The paper revisits the security properties of DPoS and reinforces the fairness and randomness for the algorithm without sacrificing its performance. We first scrutinize the limitations of the DPoS consensus, including predictability of producers, bribing producers, and lack of task incentivizing. We then introduce a cascade progressive-like ranking to judge the behaviour of the producers, and the producer who does not contribute to the network at the current interaction will be committed to a lower chance of being qualified for the coming involvement. More specifically, we conceptualize the tweaking parameters and the weights for the producers and bring in the weighted committee to measure the contributions of the producers. The weighted committee is a multiset derived from the committee and the weights specify the multiplicities of the elements (the producers). A pseudo-random process is also formulated to squeeze out the producer probabilistically from the weighted committee for creating the blocks. This unpredictability of the producers amplifies the randomness of the network and gives rise to a more secure and reliable cryptocurrency system. The proposal aspires to be beneficial for practical applications and the practitioners are as well encouraged to customize case-by-case the tweaking parameters and the pseudo-random process.

Nafiz Al Asad, Md. Tausif Elahi, Abdullah Al Hasan, Mohammad Abu Yousuf

Electronic exchange of healthcare data between hospitals and institutions is limited by privacy, interoperability, and dependency on centralized data management systems. Sharing patients' data without their consent is a violation of the law and bears the risk of identity hack and misuse of healthcare data. Not all healthcare institutions follow a similar structure and semantic to store patients' data, making it almost impossible to share the data for research and better patient management. Centralized data management system is vulnerable to cyberattacks and data theft. It also creates lock-in problems for patients intending to migrate their data to any other institute. In this paper, a permissioned Blockchain with Proof of Authority (PoA) technology has been proposed that guarantees data privacy, data owner's control on sharing their sensitive information, and effective distributed management of healthcare records. It also addresses the importance of managing medical records in an interoperable manner, and the use of de facto standards such as Fast Healthcare Interoperable Resources (FHIR), which helps the meaningful exchange of healthcare records among all participants. The work is based on simulating the outcome of using PoA to understand the efficacy of this consensus algorithm when using Blockchain technology for secure data sharing.