Blockchain Papers

Gaosheng Tan, Rui Zhang, Hui Ma, Yang Tao

No abstract is available for this record.

Simon Mangel, Lars Gleim, Jan Pennekamp, Klaus Wehrle · 5 authors

No abstract is available for this record.

Mohame Ikbal Nacer, Simant Prakoonwit, Ismail Alarab

No abstract is available for this record.

F. W. Dekker, Zekeriya Erkin

No abstract is available for this record.

Stefan Driessen, Geert Monsieur, Willem‐Jan van den Heuvel, Damian A. Tamburri

No abstract is available for this record.

Jing Pan, Xiaofeng Chen, Fangguo Zhang, Willy Susilo

No abstract is available for this record.

Arij Alfaidi, Edward Chow

No abstract is available for this record.

Alaa Omran Almagrabi, Ali Kashif Bashir

The design of distributed ledger, Asymmetric Key Algorithm (AKA) blockchain systems, is prominent in administering security and access control in various real-time services and applications. The assimilation of blockchain systems leverages the reliable access and secure service provisioning of the services. However, the distributed ledger technology’s access control and chained decisions are defaced by pervasive and service unawareness. It results in degrading security through unattended access control for limited-service users. In this article, a service-aware access control procedure (SACP) is introduced to address the afore-mentioned issue. The proposed SACP defines attended access control for all the service session by identifying the users and service provider availability. The distributed nature of the ledger systems and classification tree learning are combined to determine unattended access. The sole access is determined by summarizing the closed and open access requests and the service provider’s availability and integrity checks. In this process, the learning process classifies the secured access request and completed the integrity checks of the current and previous service dissemination. This classification-based access administration reduces the service disconnections and false access rate of the applications.

Spyridon Nikolaidis, Ioannis Refanidis

No abstract is available for this record.

Efe Seyitoglu, Attila A. Yavuz, Thang Hoang

Proof-of-Work (PoW) is one of the fundamental and widely-used consensus algorithms in blockchains. In PoW, nodes compete to receive the mining reward by trying to be the first to solve a puzzle. Despite its fairness and wide-availability, traditional PoW incurs extreme computational and energy waste over the blockchain. This waste is considered to be one of the biggest problems in PoW-based blockchains and cryptocurrencies. In this work, we propose a new useful PoW called Proof-of-Useful-Randomness (PoUR) that mitigates the energy waste by incorporating pre-computed (disclosable) randomness into the PoW. The key idea is to inject special randomness into puzzles via algebraic commitments that can be stored and later disclosed. Unlike the traditional wasteful PoWs, our approach enables pre-computed commitments to be utilized by a vast array of public-key cryptography methods that require offline-online processing (e.g., digital signature, key exchange, zero-knowledge protocol). Moreover, our PoW preserves the desirable properties of the traditional PoW and therefore does not require a substantial alteration in the underlying protocol. We showed the security of our PoW, and then fully implemented it to validate its significant energy-saving capabilities.

Sara Rouhani, Ralph Deters

Trust is the main barrier preventing widespread data sharing. The lack of transparent infrastructures for implementing data trust prevents many data owners from sharing their data and concerns data users regarding the quality of the shared data. Data trust is a paradigm that facilitates data sharing by forcing data users to be transparent about the process of sharing and reusing data. Blockchain technology proposes a distributed and transparent administration by employing multiple parties to maintain consensus on an immutable ledger. This paper presents an end-to-end framework for data trust to enhance trustworthy data sharing utilizing blockchain technology. The framework promotes data quality by assessing input data sets, effectively manages access control, and presents data provenance and activity monitoring. We introduce an assessment model that includes reputation, endorsement, and confidence factors to evaluate data quality. We also suggest an adaptive solution to determine the number of transaction validators based on the computed trust value. The proposed data trust framework addresses both data owners' and data users' concerns by ensuring the trustworthiness and quality of the data at origin and ethical and secure usage of the data at the end. A comprehensive experimental study indicates the presented system effectively handles a large number of transactions with low latency.

Zhiyuan Xue, Miao Wang, Qiuyue Zhang, Yunfeng Zhang · 5 authors

Blockchain is a decentralized distributed ledger technology. The public chain represented by Bitcoin and Ethereum only realizes the limited anonymity of user identity, and the transaction amount is open to the whole network, resulting in user privacy leakage. Based on the existing anonymous technology, the concealment of the sender, receiver, amount of the transaction, and does not disclose any information, which makes the supervision difficult. Therefore, the design of blockchain scheme with privacy protection and supervision functions is of great significance. In this paper, a blockchain transaction model with both privacy and supervision function is proposed. It uses probability encryption to realize the hiding of the true identity of the blockchain transaction, and uses the commitment scheme and zero-knowledge proof technology to realize the privacy protection and guarantee legitimacy verification of the transaction. With the use of encryption technology, the regulators can supervise blockchain transactions without storing the users' information, which greatly reduces the pressure on storage, computing and key management. In addition, it does not rely on specific consensus mechanism and can be used as an independent module. The security performance analysis shows that the proposed scheme has great practicability and has potential application in many fields.

Gabriel de Sousa Matsumura, Luciana Brasil Rebelo dos Santos, Arlindo Flávio da Conceição, Nandamudi L. Vijaykumar

Smart Contracts (SCs) are programs stored in a Blockchain to ensure agreements between two or more parties. Due to the unchangeable essence of Blockchain, failures or errors in SCs become perpetual once published. The reliability of SCs is essential to avoid financial losses. So, SCs must be checked to ensure the absence of errors. Hence, many studies addressed new methods and tools for zero-bug software in SCs. This paper conducted a systematic literature mapping identifying initiatives and tools to analyze SCs and how to deal with the identified vulnerabilities. Besides, this work identifies gaps that may lead to research topics for future work.

Jie Shen, Jiajun Zhou, Yunyi Xie, Shanqing Yu · 5 authors

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.

Jiacheng Liang, Songze Li, Cao, Bochuan, Wensi Jiang · 5 authors

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.

Jiaxi Liu, Peihang Liu, Zhonghong Ou, Guangwei Zhang · 5 authors

No abstract is available for this record.

Shanxuan Chen, Jia Zhu, Zhihao Lin, Jin Huang · 5 authors

No abstract is available for this record.

Matteo Campanelli, Hamidreza Khoshakhlagh

No abstract is available for this record.

Shihui Fu, Guang Gong

No abstract is available for this record.

Yingwen Chen, Linghang Meng, Huan Zhou, Guangtao Xue

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.

Baobao Chai, Biwei Yan, Anming Dong, Jiguo Yu

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.

Ibrahim Tariq Javed, Fares Alharbi, Tiziana Margaria, Noël Crespi · 5 authors

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.

Gyeong-Jin Ra, Taehoon Kim, Im-Yeong Lee

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.