The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph. Furthermore, we propose a generic end-to-end graph neural network model, named $\text{I}^2 \text{BGNN}$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.
We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to malicious model owners who intend to evade payment. OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment. In OmniLytics, a model owner splits its model into the private and public parts and publishes the public part on the contract. Through the execution of the contract, the participating data owners securely aggregate their locally trained models to update the model owner's public model and receive reimbursement through the contract. We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost, execution time, and model quality under various parameter combinations. For training a CNN on the MNIST dataset, the MO is able to boost its model accuracy from 62% to 83% within 500ms in blockchain processing time.This demonstrates the effectiveness of OmniLytics for practical deployment.
The rapid development of wearable sensors and the 5G network empowers traditional medical treatment with the ability to collect patients’ information remotely for monitoring and diagnosing purposes. Meanwhile, the health‐related mobile apps and devices also generate a large amount of medical data, which is critical for promoting disease research and diagnosis. However, medical data is too sensitive to share, which is also a common issue for IoT (Internet of Things) data. The traditional centralized cloud‐based medical data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single‐point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the blockchain‐based smart contract technique enables the programmability for participants to access the data. All the interactions are authenticated and recorded by the other participants of the blockchain network, which is tamper resistant. In this paper, we leverage the K‐anonymity and searchable encryption techniques and propose a blockchain‐based privacy‐preserving scheme for medical data sharing among medical institutions and data users. To be specific, the consortium blockchain, Hyperledger Fabric, is adopted to allow data users to search for encrypted medical data records. The smart contract, i.e., the chaincode, implements the attribute‐based access control mechanisms to guarantee that the data can only be accessed by the user with proper attributes. The K‐anonymity and searchable encryption ensure that the medical data is shared without privacy leaking, i.e., figuring out an individual patient from queries. We implement a prototype system using the chaincode of Hyperledger Fabric. From the functional perspective, security analysis shows that the proposed scheme satisfies security goals and precedes others. From the performance perspective, we conduct experiments by simulating different numbers of medical institutions. The experimental results demonstrate that the scalability and performance of our scheme are practical.
With the development of Internet of things, more and more objects are connected to the Internet. It will lead to data leakage caused by arbitrary access easily. Thus, an effective method to prevent data leakage is needed. Traditional access control schemes include role-based access control (RBAC), attribute-based access control (ABAC), and capability-based access control (CapBAC) are all using a central or a third trusted party to manage user information, which is prone to single failure. In order to solve the problems, in this paper, a fine-grained access control scheme based on blockchain technology named SFAC is proposed. In recent years, blockchain technology has been widely studied and applied in various fields. It has the advantages of decentralization, imitation tampering, and traceability, and is a distributed public ledger. Therefore, combined with blockchain, SFAC can not only solve the problem of centralization, but also allow users to request resources in batches, and uses token mechanism to facilitate users’ requests. After the user gets a token, it can directly use the token to revoke resources. Besides, we use a smart contract to implement our proposed scheme and carrying out a series of experiments on the test network of Ethereum. The experimental results show that our SAFC is feasible.
With the increasing use of smart devices and sensors, enormous amounts of data are being generated continuously. The data is commonly stored in centralized cloud platforms and consumed by different services. The data is indeed a valuable resource for many service providers who provide advanced features and utilities to their subscribers. However, user data include personal and sensitive information which can be misused in many ways. There is no way for a subscriber to confirm that their service provider is compliant with data privacy regulations. The existing privacy enhancing techniques such as anonymization and differential privacy substantially reduce data usability while ensuring privacy. Therefore, it remains essential to provide a feasible solution that allows service providers to take advantage of user data while guaranteeing their privacy. In this paper, we present PETchain: a novel privacy enhancing technology using blockchain and smartcontract. In PETchain, data is stored securely in a distributed manner and processed in a user-selected trusted execution environment. Users deploy the smartcontract that allows them to decide whether and how their data can be exploited by service providers. The feasibility and performance of PETchain are presented by implementing PETchain over a consortium Ethereum blockchain.
The human internet of things (HIoT) is a promising trend that adopts a user-centered vision to improve life quality by interacting with heterogeneous physical and virtual entities and the internet. However, It refers to exchanging contextual data between collaborative entities that raise privacy concerns. Emerging blockchain technology allows a digital identity management system (IDM) to be deployed in it, which largely alleviates the problems caused by the centralized third party. Still, its inherent transparency and lack of privacy pose a considerable challenge to IDM. We propose verifiable anonymous identity management (VAIM) connecting privacy channels between users by constructing identity verification and access control provisioning via user-centric decisions and an anonymous identity management system. This work has the following contributions: (1) We establish a novel IDM system by analyzing the existing scheme. In this regard, we improve the traditional claim identity model in blockchain by implementing zero-knowledge proof (ZKP) algorithms to achieve identity unlinkability, essentially preventing the disclosure of attribute ownership. (2) We implement a system that includes blind ordered multi-signature (BOMS) protocol, which allows users to processes efficiently and trusts the verification of anonymous transactions. (3) Finally, specific ZKP-based algorithm (commonly used practical ZKP such as Camenisch and Lysyanskaya signature (CL-Signature) and zero-knowledge succinct non-interactive argument of knowledge (ZK-SANRKS)) implementation and various environment performance evaluation and security analysis show that our scheme achieves efficient privacy protection and a broader application scope compared with the prior model. To the best of our knowledge, existing zero-knowledge proof-based IDM has not developed or compared each scheme before.
Rafael Torres Moreno, Jesús García-Rodríguez, Jorge Bernal Bernabé, Antonio Skármeta
Identity Management (IdM) systems have traditionally relied on a centralized model prone to privacy, trust, and security problems, like potential massive data breaches or identity spoofing. Identity providers accumulate excessive power that might allow them to become a big brother, analyzing and storing as much data as possible. Users should be able to trust identity providers and manage their personal information straightforwardly without compromising their privacy. The European OLYMPUS project introduces a distributed approach for IdM based on enhanced Attribute-Based Credentials (ABC) that splits the role of Identity Provider to limit their influence and chances to become a unique point of failure. However, the trust relationship between service providers, users, and identity providers is still a gap in those kinds of privacy-preserving ABC systems. Decentralized technologies are an opportunity to break away from the centralized model and propose systems that respect privacy while increasing users' trust. This paper presents an evolution of the OLYMPUS architecture, maintaining all the privacy features and incorporating distributed ledger technologies to enhance trust and security in online transactions and IdM systems. The proposed system has been implemented, tested, and validated, showing its performance and feasibility to manage user's identity in a fully privacy-preserving, distributed and reliable way.
Laura Ricci, Damiano Di Francesco Maesa, Alfredo Favenza, Enrico Ferro
Several blockchain projects to help against COVID-19 are emerging at a fast pace, showing the potential of this disruptive technology to mitigate the multi-systemic threats the pandemic is posing on all phases of the emergency management and generate value for the economy and society as a whole. This survey investigates how blockchain technology can be useful in the scope of supporting health actions that can reduce the spread of COVID-19 infections and allow a return to normality. Since the prominent use of blockchains to mitigate COVID-19 consequences are in the area of contact tracing and vaccine/immunity passport support, the survey mainly focuses on these two classes of applications. The aim of the survey is to show that only a proper combination of blockchain technology with advanced cryptographic techniques can guarantee a secure and privacy preserving support to fight COVID-19. In particular, this article first presents these techniques, i.e. zero-knowledge, Diffie Hellman, blind signatures, and proxy re-encryption, then describes how they are used in combination with blockchains to define robust and privacy-preserving solutions. Finally, a brief description of blockchain applications beyond contact tracing and vaccine certification is presented.
The IoT, or Internet of Things has been a major talking point amongst technology enthusiasts in recent years. The internet of thing (IoT) has been emerged and evolved rapidly, making the world's fabric around us smarter and more responsive. The smart home uses one such transformation of IoT, which seems to be the wave of the future. However, with the increasing wide adoption of IoT, data security, and privacy concerns about how our data is collected and shared with others, has also risen. To solve these challenges, an approach to data privacy and security in a smart home using blockchain technology is proposed in this paper. We propose authentication scheme that combines attribute-based access control with smart contracts and edge computing to create a secure framework for IoT devices in smart home systems. The edge server adds scalability to the system by offloading heavy processing activities and using a differential privacy method to aggregate data to the cloud securely and privately. We present several aspects of testing and implementing smart contracts, the differential private stochastic gradient descent algorithm, and system architecture and design. We demonstrate the efficacy of our proposed system by fully examining its security and privacy goals in terms of confidentiality, integrity, and availability. Our framework achieves desired security and privacy goals and is resilient against modification, DoS attacks, data mining and linkage attacks. Finally, we undertake a performance evaluation to demonstrate the proposed scheme's feasibility and efficiency.
We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, C_V, that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communication from the prover by R-F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2^{R-F}. Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero-knowledge holds against a "semi-malicious" verifier that maliciously selects its random tape and then plays honestly. Consequently, we show that some classical HVSZK proof systems, like the one for the complete Statistical-Distance problem (Sahai and Vadhan, JACM 2003) admit randomness sparsification with no penalty. Along the way we introduce new notions of pseudorandomness against interactive proof systems, and study their relations to existing notions of pseudorandomness.
Most of the existing sharing models adopt blockchain technology. With the increase of data volume and participating nodes, the storage performance and sharing security of data cannot be guaranteed, which is prone to data leakage. To solve this problem, we propose a double-link data storage and sharing model based on the alliance chain, which combines off-chain data storage and on-chain data sharing. The off-chain database uses the Hadoop Distributed File System (HDFS) distributed file system based on mean shift clustering to store data, which improves the storage and access rate of the data. The consensus mechanism combining Delegated Proof of Stake (DPOS)and Practical Byzantine Fault Tolerance (PBFT) with copy deletion scheme is adopted to realize the safe sharing of data. The proposed model has good efficiency, security, and accuracy, and can effectively improve the problems existing in smart city data security management.
Access control is a technology that can guarantee the security of information in network transmission, in which role-based access control is to separate the subject from the permission, and to assign the permission by distributing the corresponding role of the user. However, the traditional role-based access control scheme is generally centralized, the allocation of user’s role lacks fine granularity, and there is static in the allocation of the role and permission, which is not consistent with the distributed and dynamic network architecture nowadays. Hence, we propose a dynamic and fine-grained role-based access control model DF-RBAC, which can realize the flexible assignment of roles by resource owners and security verification of assigned roles. Further, we take advantage of blockchain technology and cryptography technology to combine with the DF-RBAC framework to achieve access to the activity log security audit function, which can ensure the security of the overall architecture. By safety and experimental analysis, our framework has proved to be feasible.
We introduce and study a simple kind of proof system called line-point zero knowledge (LPZK). In an LPZK proof, the prover encodes the witness as an affine line 𝐯(t) : = at + 𝐛 in a vector space 𝔽ⁿ, and the verifier queries the line at a single random point t = α. LPZK is motivated by recent practical protocols for vector oblivious linear evaluation (VOLE), which can be used to compile LPZK proof systems into lightweight designated-verifier NIZK protocols. We construct LPZK systems for proving satisfiability of arithmetic circuits with attractive efficiency features. These give rise to designated-verifier NIZK protocols that require only 2-5 times the computation of evaluating the circuit in the clear (following an input-independent preprocessing phase), and where the prover communicates roughly 2 field elements per multiplication gate, or roughly 1 element in the random oracle model with a modestly higher computation cost. On the theoretical side, our LPZK systems give rise to the first linear interactive proofs (Bitansky et al., TCC 2013) that are zero knowledge against a malicious verifier. We then apply LPZK towards simplifying and improving recent constructions of reusable non-interactive secure computation (NISC) from VOLE (Chase et al., Crypto 2019). As an application, we give concretely efficient and reusable NISC protocols over VOLE for bounded inner product, where the sender’s input vector should have a bounded L₂-norm.
Blockchain, the foundation of Ethereum, has received greater attention on recent years as a decentralised and distributed public ledger technology that offers different properties like security, privacy, scalability and immutability during online data transactions, reduces cost of transactions, and enables verification and efficient exchange of personnel information across many computers without any trusted parties. The goal of this survey is to propel some methods in preserving user privacy during data transactions using Ethereum smart contract. We analysed the privacy challenges in blockchain and figured out the existing cryptographic mechanisms in preserving the transaction privacy. We also addressed some technique for simplifying claim settlement process in healthcare insurance. By using Ethereum blockchain via smart contract, current challenges in insurance such as being time-consuming, can be avoided thereby preventing insurance frauds and thus improving transparency to all members in-network. It also helps to avoid paper-based contracts, which are prone to errors, and removes the intermediaries. Furthermore, there is still a need to examine and consider future research in order to overcome privacy attacks when using blockchain.
Most IoT devices cannot afford to be a blockchain node due to the high computation and storage loads. Thus, the blockchain is usually deployed on one delegate node, e.g., the edge device or cloud, which may encounters three drawbacks: (1) The delegate node becomes the single failure point when the number of delegate notes are limited. (2) The delegate node replicating the blockchain data can lead to privacy information leak. (3) The delegate node is vulnerable to the Distributed Denial of Service (DDoS) attack. To tackle these drawbacks, we consider to minimize the redundant of blockchain to make the IoT devices as the specialized blockchain nodes. In this paper, we integrate a permissioned blockchain (HLF), an attribute-based access control (ABAC) and an identity-based signature (IBS) to build a security, lightweight, and cross-domain blockchain-based IoT access control system. Specifically, we divided the IoT system into different function domains, named IoT domains. Then, we establish a local blockchain ledger for each IoT domain to enable more IoT devices as blockchain nodes. The local blockchain ledger records the IoT domain entities' attributes, policy files' digests, and access decisions. Meanwhile, we use the channel technology of HLF to realize cross-domain access and use the IBS to filter the legal access requests for each IoT domain to prevent DDoS attacks. We also design a policy decision point (PDP) selection algorithm that select multiple IoT devices (blockchain nodes) to achieve the real-time distributed policy decisions (off-chain). Finally, we implement and evaluate the proposed system to demonstrate its practicality.
Xianjia Yu, Jorge Peña Queralta, Jukka Heikkonen, Tomi Westerlund
Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.
The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.
Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this paper, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.
Darrell Yonathan, Diyanatul Husna, F. Astha Ekadiyanto, I Ketut Eddy Purnama · 10 authors
This paper discusses the implementation of smart contracts on the Ethereum blockchain system for telemedicine data storage. Telemedicine is one of the currently developing digital technologies in the health and medical sectors. Telemedicine can be more efficient when seeking treatment because patients do not need to see a doctor face to face. When using blockchain technology, the stored data becomes more transparent for each node in the blockchain network but has verification on every transaction which takes time and gas costs. However, telemedicine has several risks and problems, one of which is long data storage process time because there must be a verification process first to ensure data security. Another problem faced is the issue of the gas fee of the blockchain telemedicine system which is billed in every data storage transaction. In this study, a blockchain system was introduced for managing and securing databases on telemedicine. The implementation of this blockchain system was carried out on a website page that can add data to and retrieve data from the blockchain system. The results of this study showed that blockchain was successfully implemented to store telemedicine data with Ethereum. The analysis in this paper refers to the set and gets functions. The set function is used to send data to the blockchain, and the get function is used to retrieve data from the blockchain. From testing, the Get function has a much faster execution time than the Set function because the Get function does not require verification to retrieve its data. In the iterations carried out—namely 1, 10, and 100—the longest time on average was at 100 iterations when compared to the other iterations. In the tests carried out, the more characters that were stored, the more gas costs must be paid. In the tests, the percentage increase in costs was 0.34% per character.
There has been a great deal of discussion of the challenges on privacy, data interoperability and quality of Educational Professional Personal Record (EPPR). Therefore, there is a need to reassess the current models, in which various parties generate, exchange and observe a huge amount of personal data with regard to EPPR. Ethereum blockchain has shown that trusted, auditable transactions is detectible using a decentralized network of nodes accompanied by a general ledger. Thus, due to the fast-moving development of educational and professional data generators such as online universities and distance learning, requires learners to engage in detail into their EPPR as well as the educational and professional data generators. In this paper, we propose a novel decentralized framework to manage EPPR using Ethereum blockchain technology. The framework provides the owner of the EPPR a comprehensive immutable log and ease of access to their educational records across the educational record editors and consumers. Furthermore, it provides a recommender engine to endorse skills and competencies to the education record owners and similar candidates for educational records editors and consumers. Ethereum blockchain can provide solutions in terms of exchanging of data among parties by ensuring privacy, accountability and data interoperability. The aim of the proposed framework is to enable educational stakeholders (universities and employing agencies) to participate in the network as blockchain miners rewarded by pseudonymized data in compliance with General Data Protection Rules in United Arab Emirates.
Chaosheng Feng, Keping Yu, Ali Kashif Bashir, Yasser D. Al‐Otaibi · 7 authors
The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs a smart contract for authentication and access control, public key cryptography for providing accounts and ensuring accounts' security, and a distributed ledger for security audit. In addition, to speed up out-sourced computations and reduce electricity consumption, an ABE model with parallel outsourced computation (ABEM-POC) is constructed, and a generic parallel computation method for ABE is proposed. The analysis of the experimental results shows that parallel computation significantly improves the speed of outsourced encryption and decryption compared to serial computation.
Abstract We consider the problem of proving in zero knowledge that an element of a public set satisfies a given property without disclosing the element, i.e., for some u , “ $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> and P ( u ) holds”. This problem arises in many applications (anonymous cryptocurrencies, credentials or whitelists) where, for privacy or anonymity reasons, it is crucial to hide certain data while ensuring properties of such data. We design new modular and efficient constructions for this problem through new commit-and-prove zero-knowledge systems for set membership , i.e. schemes proving $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> for a value u that is in a public commitment $$c_u$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>c</mml:mi><mml:mi>u</mml:mi></mml:msub></mml:math> . We also extend our results to support non-membership proofs , i.e. proving $$u \notin S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∉</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> . Being commit-and-prove, our solutions can act as plug-and-play modules in statements of the form “ $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> and P ( u ) holds” by combining our set (non-)membership systems with any other commit-and-prove scheme for P ( u ). Also, they work with Pedersen commitments over prime order groups which makes them compatible with popular systems such as Bulletproofs or Groth16. We implemented our schemes as a software library, and tested experimentally their performance. Compared to previous work that achieves similar properties—the clever techniques combining zkSNARKs and Merkle Trees in Zcash—our solutions offer more flexibility, shorter public parameters and $$3.7 \times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>3.7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> – $$30\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>30</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> faster proving time for a set of size $$2^{64}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mn>64</mml:mn></mml:msup></mml:math> .
Blockchain is a distributed accounting system. One of its key issues is how to ensure agreement between distrustful nodes. As an important component of blockchain, consensus algorithm can solve the consistency problem, and its efficiency directly determines the performance of blockchain. Compared with Proof of Work (PoW) and Proof of Stake (PoS), the existing Delegated Proof of Stake (DPoS) consensus algorithm improves the efficiency of consensus, but it will face some threats, such as DoS attack and collusion attack, because the mechanism that each witness node takes turns to generate blocks. Therefore, we propose a delegated proof of stake consensus algorithm with dynamic trust, that is, DT-DPoS. We improve an Eigen Trust-based trust model and construct an evaluation criterion based on the combination of stake voting and trust value, which improves the reliability of witness nodes and reduces the risk of collusion attacks. At the same time, we use a ring signature scheme, which ensures the privacy and anonymity of witness nodes. Finally, the theoretical analysis shows the effectiveness and scalability of our algorithm.