Blockchain Papers

Ya-nan Li, Tian Qiu, Qiang Tang

Cryptocurrency exchange platforms such as Coinbase, enable users to purchase and sell cryptocurrencies conveniently just like trading stocks/commodities.However, because of the nature of blockchain, when a user withdraws coins (i.e., transfers coins to an external on-chain account), all future transactions can be learned by the platform.This is in sharp contrast to conventional stock exchange where all external activities of users are always hidden from the platform.Since the platform knows highly sensitive user private information such as passport number, bank information etc, linking all (on-chain) transactions raises a serious privacy concern about the potential disastrous data breach in those cryptocurrency exchange platforms.In this paper, we propose a cryptocurrency exchange that restores user anonymity for the first time.To our surprise, the seemingly well-studied privacy/anonymity problem has several new challenges in this setting.Since the public blockchain and internal transaction activities naturally provide many non-trivial leakages to the platform, internal privacy is not only useful in the usual sense but also becomes necessary for regaining the basic anonymity of user transactions.We also ensure that the user cannot double spend, and the user has to properly report accumulated profit for tax purposes, even in the private setting.We give a careful modeling and efficient construction of the system that achieves constant computation and communication overhead (with only simple cryptographic tools and rigorous security analysis); we also implement our system and evaluate its practical performance.We would like to design a cryptocurrency exchange system that could at least restore the user's anonymity/privacy so that it is unlinkable between external transaction records and the user's real identity.

Zahra Batool, Kaiwen Zhang, Matthew Toews

Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that integrating blockchain and smart contract technologies into FL can solve the two aforementioned issues. In this paper, we present Block-RACS (Blockchain-based Reputation Aware Client Selection), a mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. Block-RACS employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows Block-RACS to measure the relative contribution of each client by calculating a Shapley value and allocating rewards accordingly. Moreover, a blockchain-based reputation mechanism enables audibility and non-repudiation. The security analysis of the system is also presented to check the security vulnerabilities. We have implemented Block-RACS using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that Block-RACS outperforms existing baseline schemes by improving accuracy and reducing the number of FL rounds.

Biegon Kipkoech Collins, Alex Kibet, Andrew M. Kahonge

Amidst rapid technological advancement and digital transformation, ensuring privacy and data security is paramount. Governments and digital service providers face the challenge of establishing secure identification systems that protect individuals' personal information while enabling reliable authentication and seamless user experiences. Traditional identification methods often require individuals to disclose sensitive personal information, leading to privacy risks and potential data breaches. Zero-knowledge proofs (ZKPs) have emerged as a promising solution to address these concerns. By leveraging ZKPs, individuals can authenticate their identities or assert specific attributes without revealing sensitive data. This approach holds great potential for preserving privacy while enabling efficient and trustworthy verification processes. This paper explored ZKPs and how governments and digital service providers can utilize this technology to achieve secure identification while upholding privacy. A key focus was prototyping a secure identification protocol using ZKPs. Through practical implementation, this research aimed to demonstrate the reliability and effectiveness of ZKPs in real-world scenarios. Keywords : zero-knowledge proofs, privacy, digital identity, governments, digital services. DOI: 10.7176/ISDE/13-2-06 Publication date: September 30 th 2023

Jing Xu, Shaokun YANG

In order to solve the problems of imperfect transaction legitimacy verification strategies for protecting account balances and transaction amounts in privacy protection of consortium blockchain, and the low efficiency of the basic encryption algorithm Paillier, a ⁺HomElG zero-knowledge proof protocol for consortium blockchain transfer privacy protection was proposed. A consortium blockchain transfer privacy protection application was constructed based on PBFT, which expounded the consensus interaction scenario of zero-knowledge proof of homomorphic encryption. The transaction amount and balance of account were encrypted by the ⁺HomElG algorithm, and the zero-knowledge proof of the ciphertext was designed with the Σ protocol. The non-interactive zero-knowledge was designed through the idea of the Fiat-Shamir algorithm processes such as the proof of equality, the amount of the transaction greater than zero and the balance of the transfer party not less than zero in the proof of range. The protocol was proved to be correct, complete and zero-knowledge under the DDH. A consortium blockchain transfer privacy protection prototype system based on Hyperledger Fabric was constructed. The results verified that the protocol can realize ciphertext transactions to protect balance of account and transaction amount under the condition of non-interactive zero-knowledge proof. When the key length is 3072 bit and the data length is a 12-bit decimal integer, the efficiency of the ⁺HomElG algorithm is 150.3 ms, and the efficiency of the proof of equality, the amount of the transaction greater than zero and the balance of the transfer party not less than zero in the proof of range are 482.3 ms, 209.3 ms and 261.3 ms respectively. Compared with the existing protocols, the proposed ⁺HomElG algorithm is more efficient, and its transaction legitimacy verification strategies such as equality proof and range proof are more perfect and efficient. The proposed protocol can meet the privacy protection requirements of consortium blockchain transfer transactions.

Muhammad Irfan Khalid, Mansoor Ahmed, Jungsuk Kim

Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.

Princy Diwan, Ramgopal Kashyap, Brijesh Khandelwal

Summary Recently, blockchain utilization with advanced encryption and decryption has gained improved attention over diverse applications. Especially in the medical field, big data storage requires an effective medium for managing user data with high confidentiality. The existing research presents diverse encryption algorithms, but certain drawbacks include sensitive information leakage, ineffective data integrity verification, and increased computational cost and time. The proposed work develops an effective blockchain mechanism for secured data storage to conquer these limitations. This article secures the medical research data from different team members for intellectual share estimation. Initially, the registration process involves user details such as name, contact number, age and so on. Next, a digital signature is generated for every registered user using the SHA‐256 hash algorithm. During data uploading, the data is encrypted using elliptic curve Diffie Hellman encryption scheme to promote security. The audio and video files are stored in inter planetary file system, and texts are stored in the blockchain. The users can retrieve the stored data from the blockchain using the encryption key. The identities and associated public keys are verified using a smart contract to validate the user certificate. Next, a decision form is made to show the polling mechanism using item preference ranking for other users viewing the data. The proposed mechanism ensures intellectual shares for all the users associated with a specified project. The performances are analyzed using PYTHON, and the encryption time obtained in the proposed model is 0.85 s at 140 security bit level.

Jim Woodcock, Mikkel Schmidt Andersen, Diego F. Aranha, Stefan Hallerstede · 11 authors

This report describes the state of the art in verifiable computation. The problem being solved is the following: The Verifiable Computation Problem (Verifiable Computing Problem) Suppose we have two computing agents. The first agent is the verifier, and the second agent is the prover. The verifier wants the prover to perform a computation. The verifier sends a description of the computation to the prover. Once the prover has completed the task, the prover returns the output to the verifier. The output will contain proof. The verifier can use this proof to check if the prover computed the output correctly. The check is not required to verify the algorithm used in the computation. Instead, it is a check that the prover computed the output using the computation specified by the verifier. The effort required for the check should be much less than that required to perform the computation. This state-of-the-art report surveys 128 papers from the literature comprising more than 4,000 pages. Other papers and books were surveyed but were omitted. The papers surveyed were overwhelmingly mathematical. We have summarised the major concepts that form the foundations for verifiable computation. The report contains two main sections. The first, larger section covers the theoretical foundations for probabilistically checkable and zero-knowledge proofs. The second section contains a description of the current practice in verifiable computation. Two further reports will cover (i) military applications of verifiable computation and (ii) a collection of technical demonstrators. The first of these is intended to be read by those who want to know what applications are enabled by the current state of the art in verifiable computation. The second is for those who want to see practical tools and conduct experiments themselves.

Peiran Wang

Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work) is one of the most popular consensus protocols. However, the PoW consensus protocol which incentives the participants to use their computing power to solve a meaningless hash puzzle is continuously questioned as energy-wasting. To address these issues, we propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain). We first propose a secure and robust blockchain architecture that takes federated learning tasks as proof of work. Then a pool aggregation mechanism is integrated to improve the efficiency of the FedChain architecture. To protect model parameter privacy for each participant within a mining pool, a secret sharing-based ring-all reduce architecture is designed. We also introduce a data distribution-based federated learning model optimization algorithm to improve the model performance of FedChain. At last, a zero-knowledge proof-based federated learning model verification is introduced to preserve the privacy of federated learning participants while proving the model performance of federated learning participants. Our approach has been tested and validated through extensive experiments, demonstrating its performance.

Goshgar Ismayilov, Can Özturan

Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analysed. We propose a Private Token Transfer System (PTTS) for the Ethereum public blockchain in the first part of this paper. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. With the help of web user interface designed, the end users can interact with the smart contract without any third-party setup. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack which has been modelled as a network flow problem. It is shown that in case some balance ranges are deliberately leaked out to particular organizations or adversial entities, it is possible to extract meaningful information about the user balances by employing minimum cost flow network algorithms that have polynomial complexity. The experimental study reports the Ethereum gas consumption and proof generation times for the proposed framework. It also reports network solution times and goodness rates for a subset of addresses under the balance range privacy attack with respect to number of addresses, number of transactions and ratio of leaked transfer transaction amounts.

Goshgar Ismayilov, Can Özturan

The privacy-preserving data aggregation is a critical problem for many applications where multiple parties need to collaborate with each other privately to arrive at certain results. Blockchain, as a database shared across the network, provides an underlying platform on which such aggregations can be carried out with a decentralized manner. Therefore, in this paper, we have proposed a scalable privacy-preserving data aggregation protocol for summation on the Ethereum blockchain by integrating several cryptographic primitives including commitment scheme, asymmetric encryption and zero-knowledge proof along with the hypercube network topology. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification. The analysis of the protocol is made with respect to two main perspectives as security and scalability including computational, communicational and storage overheads. In the paper, the zero-knowledge proof, smart contract and web user interface models for the protocol are provided. We have performed an experimental study in order to identify the required gas costs per individual and per system. The general formulation is provided to characterize the changes in gas costs for the increasing number of users. The zero-knowledge proof generation and verification times are also measured.

Wangjing Jia, Tao Xie, Baolai Wang

With the increasing presence of blockchain-based distributed applications in various aspects of daily life, there has been a growing focus on the privacy protection of blockchain ledgers and the corresponding regulatory technologies. However, current mainstream solutions primarily concentrate on the verifiable encryption of blockchain transaction addresses and contents, neglecting the regulatory requirements for private transactions. Moreover, the few monitorable solutions suffer from issues such as excessive centralization and a single-minded approach to regulatory content. To address these deficiencies, this paper proposes a blockchain privacy-preserving scheme that supports multi-level regulation through the utilization of zero-knowledge proofs (zk-SNARKs) and attribute-based encryption (ABE). Firstly, by leveraging zk-SNARKs, this scheme achieves blockchain privacy-preserving within an account model, enabling the concealment of user transaction addresses and values. Secondly, by employing attribute-based encryption, a multi-level regulatory model is developed alongside the privacy protection measures, allowing for selective disclosure of transaction content. Finally, we analyze the security of the proposed scheme and compare it with other schemes, discussing its advantages in terms of privacy, security, and regulatory capabilities, we also provide a preliminary evaluation of the scheme's efficiency through experiments. In conclusion, the scheme demonstrates strong privacy by relying on mathematical proofs through zk-SNARKs to ensure security while comprehensively safeguarding content. It also achieves multi-level regulation on the foundation of privacy protection, with comprehensive regulatory coverage and decentralized regulatory authority.

Syed Sajid Ullah, Vladimir Oleshchuk, Harsha S. Gardiyawasam Pussewalage

The Internet of Things (IoT) network is rapidly expanding due to sudden technological advancements, which enable objects to become intelligent and contribute to the network. Before permitting a newly added IoT device to communicate with the network, it is essential to provide access and authenticate the device’s legitimacy by ensuring that it has not been tampered. Recently, blockchain technology has been integrated into Attribute Based Access Control (ABAC) protocols to supply a more robust security mechanism for access control in IoT. The IoT and blockchain-based ABAC serve as the foundation of this survey, offering a comprehensive introduction to both topics. Additionally, several security concerns and vulnerabilities associated with Blockchain Envisioned ABAC (BE-ABAC) are presented. A Comparison with related surveys has also been made. Besides, we also present a comparative analysis based on evaluation based on Distance from Average Solution (EDAS) to rank the best schemes among the suggested BE-ABAC schemes. In conclusion, we discuss some open research challenges in an IoT network that uses blockchain to manage access control.

Sheng Hu

Data security and privacy protection are critical challenges that constrain the advancement of edge computing. Similarly, blockchain technology faces constraints in addressing security issues linked with edge computing due to its scalability limitations. To tackle these challenges and promote the development of blockchain technology, this paper presents a scheme that enhances privacy data protection in blockchain smart contracts using edge computing and a master-slave multichain architecture. Firstly, we propose a master-slave multichain architecture based on the traditional single chain and integrate it with a three-layer edge computing structure to address security issues on the edge side. We also design a signature authentication scheme utilizing ECC integrated with blockchain encryption technology. Secondly, we incorporate the role-based access control (RBAC) model with smart contracts to finely divide user privileges, construct an interdomain role-based access control (ID-RBAC) model, and provide detailed access authentication process designs for both within and between domains. Finally, experimental results demonstrate that our proposed scheme can effectively resist various attacks, significantly improve algorithm efficiency, and maintain a system overhead of less than 160 p, with a maximum transaction throughput of nearly 310 tx/s.

Piyush Kumar Shukla, Dragan Pamučar, Omar Alqahtani, Ashish Dwivedi · 6 authors

Abstract Internet of Things (IoT) has rapidly expanded with the interconnection of various devices through wireless networks. However, this widespread deployment of IoT devices has posed challenges in managing access to device resources due to their vast quantity and scale. As these IoT devices generate and share sensitive data, ensuring secure access becomes paramount. Traditional access control systems like Discretionary Access Control (DAC), Intelligent Dynamic Bandwidth Access Control (IBAC), and Mandatory Access Control (MAC) have limitations such as centralization, single-point failure, and low throughput, making them less suitable for dynamic and diverse IoT environment. To address these challenges, Role-Based Access Control (RBAC) emerges as a promising solution as it provides fine-grained and dynamic access control based on user attributes, resource attributes, action attributes, environmental attributes, permissions, and environmental context. In parallel, Blockchain Technology (BCT) has gained attention as a distributed data management system, ensuring data integrity through decentralization, immutability, and consensus mechanisms. Kafka-based Hyperledger Fabric, an open-source blockchain platform, offers more efficient consensus mechanisms, higher throughputs, and support for multiple organizations and ledgers. This study proposes to develop a novel "KafkaIoT" system, which integrates Kafka-based Hyperledger Fabric with RBAC to create a distributed access control system for IoT. By leveraging blockchain's distributed architecture, KafkaIoT addresses the challenges of record tracking, dynamic access control management, and access control in the IoT landscape. The seamless integration of BCT with IoT can enhance security and enable the development of distributed IoT-based cryptocurrency services, contributing to a safer and more reliable IoT ecosystem.

Jiachi Chen, Mingyuan Huang, Zewei Lin, Peilin Zheng · 5 authors

With the increasing popularity of cryptocurrencies and blockchain technologies, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the Smart Contract Weakness Classification (SWC) list plays an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing the open card-sorting method to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. The SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, the SWE is a scalable and continuously iterative program. We propose two update mechanisms for the maintenance of the SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to the SWE.

Xin Liu, Ruxue Wang, Dan Luo, Gang Xu · 7 authors

With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.

Andrea De Salve, Damiano Di Francesco Maesa, Paolo Mori, Laura Ricci · 5 authors

The recent interest for decentralised systems and decentralisation of the control over users’ data brings a shift in the way identities and their information are managed. Self Sovereign Identity (SSI) has been proposed as the next generation paradigm for decentralised identity management. Research on SSI is getting more and more traction, focusing mainly on the management of users’ identifiers and on providing a standard way to express and verify credentials. Instead, this paper focuses on the understanding of the role of trust in SSI and it provides new insight into the trust relationships existing between the different SSI actors. Indeed, the analysis of such roles and the relationships existing between SSI actors reveals that the current paradigm suffers from trust issues between the verifier and the issuer of a verifiable credential. In order to cope this problem, the paper proposes a new multi-layer framework that exploits trust relationships defined by the actors of the SSI standards (verifiers and issuers of verifiable credentials). An implementation of the framework through Solidity smart contracts has been proposed and deployed on both private and public blockchain networks in order to assess its capabilities. In addition, a dataset related to the spread of spam reviews has been exploited to test the benefits and performance of the proposed framework, demonstrating that it is able to improve the reliability of the SSI paradigm in real-world scenario.

Ghareeb Falazi, Uwe Breitenbücher, Frank Leymann, Stefan Schulte · 5 authors

Blockchains have become increasingly important in recent years and have expanded their applicability to many domains beyond finance and cryptocurrencies. This adoption has particularly increased with the introduction of smart contracts, which are immutable, user-defined programs directly deployed on blockchain networks. However, many scenarios require business transactions to simultaneously access smart contracts on multiple, possibly heterogeneous blockchain networks while ensuring the atomicity and isolation of these transactions, which is not natively supported by current blockchain systems. Therefore, in this work, we introduce the Transactional Cross-Chain Smart Contract Invocation (TCCSCI) approach, which supports such distributed business transactions while ensuring their global atomicity and serializability. The approach introduces the concept of Resource Manager Smart Contracts (RMSCs), and 2PC for Blockchains (2PC4BC), a client-driven Atomic Commit Protocol (ACP) specialized for blockchain-based distributed transactions. We validate our approach using a prototypical implementation, evaluate its introduced overhead, and prove its correctness.

Eisuke Tomiyama, Hiroshi Esaki, Hideya Ochiai

In recent years, machine learning models have evolved, and the training of these models requires large amounts of data. However, the training data often contains sensitive information, raising privacy concerns. Federated Learning has been proposed as a solution to mitigate privacy risks. Despite its advantages, Federated Learning still faces challenges such as the aggregator being a single point of failure, the existence of malicious participants, and the lack of incentives. Combining Federated Learning with blockchain technology could potentially address these challenges. In this study, we propose a new method for asynchronous Federated Learning using blockchain smart contracts. Our proposed method operates autonomously and in a decentralized manner without the need to trust any central organization, making it trustless. We propose an algorithm that motivates workers to submit high-quality models as quickly as possible. Workers’ behaviors are driven by incentive mechanisms. We deployed a smart contract on a local Ethereum blockchain and executed multiple workers. Our evaluation results demonstrate that learning converges and achieves accuracy comparable to conventional Federated Learning, indicating the effectiveness of our proposed method.

Yathin Kethepalli, Rony Joseph, Sai Raja Vajrala, Jashwanth Vemula · 5 authors

Crypto-wallets or digital asset wallets are a crucial aspect of managing cryptocurrencies and other digital assets such as NFTs. However, these wallets are not immune to security threats, particularly from the growing risk of quantum computing. The use of traditional public-key cryptography systems in digital asset wallets makes them vulnerable to attacks from quantum computers, which may increase in the future. Moreover, current digital wallets require users to keep track of seed-phrases, which can be challenging and lead to additional security risks. To overcome these challenges, a new algorithm is proposed that uses post-quantum cryptography (PQC) and zero-knowledge proof (ZKP) to enhance the security of digital asset wallets. The research focuses on the use of the Lattice-based Threshold Secret Sharing Scheme (LTSSS), Kyber Algorithm for key generation and ZKP for wallet unlocking, providing a more secure and user-friendly alternative to seed-phrase, brain and multi-sig protocol wallets. This algorithm also includes several innovative security features such as recovery of wallets in case of downtime of the server, and the ability to rekey the private key associated with a specific username-password combination, offering improved security and usability. The incorporation of PQC and ZKP provides a robust and comprehensive framework for securing digital assets in the present and future. This research aims to address the security challenges faced by digital asset wallets and proposes practical solutions to ensure their safety in the era of quantum computing.

Songjiang Li, Tao Zhou, Huamin Yang, Peng Wang

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.

Armin Memar Zahedani, Jelle Vos, Zekeriya Erkin

Double auctions are procedures to trade commodities such as electricity or parts of the wireless spectrum at optimal prices. Buyers and sellers inform the auctioneer what quantity they want to buy or sell at specific prices. The auctioneer aggregates these offers into demand and supply curves and finds the intersection representing the optimal price. In this way, commodities exchange owners in an economically-efficient manner. Ideally, the auctioneer is a trusted third party that does not abuse the information they gain. However, the offers reveal sensitive information about the traders, which the auctioneer may use for economic gain as insider information. These concerns are not theoretical; investigations against auctioneers in electricity and advertisement auctions for manipulating auctions are ongoing. These concerns call for solutions that conduct double auctions in a privacy-preserving and verifiable way. However, current solutions are impractical: To the best of our knowledge, the only solutions satisfying these properties require full interaction of all participants. In this work, we design a more practical solution. We propose the first privacy-preserving and verifiable double auction scheme that does not require traders to interact actively, tailored to electricity trading on (inter)national exchanges. Our solution relies on homomorphic encryption, commitments, and zero-knowledge proofs. In a simulated auction with 256 traders, we observe that traders take up to 10 seconds to generate their order, the auctioneer takes 10 seconds to verify an order, and the auction result is computed and verified in 30 seconds. We extrapolate these results to larger auctions to show the practical potential.

Stefan Pancari, Anik Rashid, Jason Zheng, Shirali Patel · 6 authors

Despite the lack of blockchain systems being utilized in modern IoT environments, the prevalence of blockchain technology is increasing, due to its high level of security and accountability. The integration of blockchain technology and access control in a decentralized system for smart home networks is a promising solution to this issue. This paper compares the implementation of attribute-based access control (ABAC) with two popular blockchain platforms, Ethereum and Hyperledger Fabric, for a smart home internet of things (IoT) environment. We present a comprehensive summary of access-control and blockchain-access-control methods, to provide the necessary background for this study. Additionally, we present an original ABAC smart contract for Ethereum, and the modification of a pre-existing Hyperledger Fabric ABAC smart contract, for this comparison. Through the simulation of both implementations, the advantages and limitations will be considered, to determine which is better suited for a smart home IoT environment.

Siben Tian, Fenhua Bai, Tao Shen, Chi Zhang · 5 authors

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm’s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.