Blockchain Papers

Mohammad Reza Nosouhi, Shui Yu, Wanlei Zhou, Marthie Grobler · 5 authors

No abstract is available for this record.

Nida Khan, Mohamed Nassar

Blockchain is a revolutionary technology promising a new generation of decentralized, transparent and tamper-proof database systems. However, one of the main challenges of the emerging blockchain systems remains to address data privacy. In Bitcoin and similar e-cash systems, privacy is limited to anonymity and addressed by using cryptographic pseudonyms for identification and authentication of parties. Privacy has more breadth in systems maintaining actual data such as medical records or energy meters. In this paper, we look into recently proposed privacy-preserving blockchains, analyze their strengths and weaknesses, and highlight research questions requiring future investigation. We conclude that privacy-preserving blockchains are still in their infancy and more research is definitely needed in this area.

Shuai Zeng, Yong Yuan, Fei–Yue Wang

The privacy issues become a major problem that should be resolved for the existing centralized online social networks, which have prompted researchers to consider the decentralization framework for online social networks. In this paper, we propose a novel decentralized social networking architecture enhanced by blockchain technology. We use a sharding framework to increase the system scalability, a blockchain system to ensure the data integrity and consistency, a reputation-based authority control method to improve the system security.

Shahidatul Sadiah, Toru Nakanishi

Recently, crowd sensing has been intensively researched, due to the rapid growth of sensor-integrated mobile devices. Crowd sensing is a participatory sensing service where a server gathers and analyzes sensing data submitted from mobile devices of lots of users. In crowd sensing, the user's anonymity is desired, since the server gathers sensitive data from the participants including their GPS locations and moving path. However, the anonymous data submission may compromise the trust of the sensing data, because anonymous users may submit inappropriate sensing data, but they cannot be traced. Therefore, as the system to achieve both anonymity and trust in crowd sensing, ARTSense has been proposed. In the system, the trust of the sensing data is assessed on the sensed environment, other users' sensing, and the reputation of the user, and furthermore the reputation of the user is anonymously managed on the feedback from the trust assessment for the data. However, the anonymous reputation system of ARTSense has the efficiency problem, i.e., the user needs to wait a random time after the data submission phase before requesting the reputation update, which causes the communication delay. In this paper, we propose an efficient anonymous reputation system for crowd sensing, which can be integrated to the trust assessment in ARTSense. In the proposed system, during the data submission, the reputation update is anonymously completed. This is because the server does not manage the reputation of each user, but each user manages his/her reputation in the user side, where the the validity of the reputation is ensured by a certificate and anonymously checked by zero-knowledge proofs. Therefore, the proposed system achieves the better efficiency with no delay.

Wie Liang Sim, Hui Na Chua, Mohammad Tahir

Identity symbolizes a personal level of an individual and becomes an embodiment of who we are, and Identity Management in Information Communication Technologies field represents the basis of authenticating a specific individual. Therefore, it is important to protect and control identity as personal information so that unfortunate misconduct would be avoided. Currently, how individuals are represented in the digital ecosystem appears to be permissive with the management of centralized control on identity. Recently, blockchain technology has been making its mark as a revolutionized approach to exceed expectations with its decentralized approach. Popularized with the cryptocurrency, blockchain technology could also create a disruptive effect in the area of identity management. In the domain of identity management, privacy protection is crucial to the security of personal data. However, there is a lack of systematic study on how the identity management aspect of blockchain technology supports personal data protection based on the requirement of regulations. In this paper, we aim to close this gap through a systematic review to examine how the identity management aspect of blockchain technology supports or contradicts personal data protection based on the requirements of the General Data Protection Regulation (GDPR) requirements.

Akram Alkouz, Ahmed HaiYasien, Abdulsalam Alarabeyyat, Khalid Samara · 5 authors

There have been recent, marked increases in the challenges of privacy, data interoperability and quality of Educational Professional Personal Record (EPPR). This calls into question the current model, in which different parties generate, exchange and monitor massive amounts of personal data related to EPPR. Ethereum blockchain has demonstrated that trusted, auditable transactions is visible using a decentralized network of nodes accompanied by a general ledger. Thus, the rapid development of educational and professional data generators such as online universities and distance learning, learners need to engage in detail into their EPPR as well as the educational and professional data generators. In this paper, we propose a novel decentralized approach to manage EPPR using Ethereum blockchain technology. The decentralized approach 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. Utilizing Ethereum blockchain features, can provide solutions with the main concerns of exchanging data between parties such as privacy, accountability and data interoperability. The aim of this approach is to also facilitate 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 (GDPR) in United Arab Emirates (UAE).

Samiran Bag, Feng Hao

In this paper we propose an end-to-end (E2E) verifiable online shareholder voting system. Our system allows different voters to have different weights associated with their votes. These weights are dependent upon the number of shares owned by voters in an organization. In our system, the voters cast their votes over the Internet using a personal computing device, say a smart-phone. The voting client interacts with an online voting server which generates encrypted ballots for the chosen candidates with a receipt. Every encrypted ballot comes with a non-interactive zero-knowledge proof to prove the well-formedness of the ciphertext. In addition, the voting system allows the voters to verify their votes are cast as intended through voter initiated auditing. All the encrypted ballots and non-interactive proofs are made available on a publicly readable bulletin board. By checking the receipt against the bulletin board, voters are assured that their votes as recorded as cast. Finally, this e-voting scheme allows everyone including third-party observers to verify all votes are tallied as recorded without involving any tallying authorities. Once the polling concludes, the tallying result is available immediately on the bulletin board with publicly verifiable audit data to allow everyone including third-party observers to verify the tallying integrity of the entire election.

Yongbin Zhao, Meng Cui, Lijuan Zheng, Rui Zhang · 7 authors

For the medical industry, there are problems such as poor sharing of medical data, tampering, and leakage of private data. In view of these problems, a blockchain-based electronic medical record access control research scheme based on the role-based access control model is proposed in this article. First, the appropriate access control strategy is adopted to solve the leakage problem of the user’s medical privacy information during the access process. Then, the information entropy technology is used to quantify the medical data, so that the medical data can be effectively and maximally utilized. Using the distributed general ledger characteristics of blockchain and its inherent security attributes, data islands can be eliminated, data sharing among medical systems can be promoted, access records can be prevented from being tampered with, and medical research and precise medical treatment can be better supported. Through this research, not only can user’s medical privacy information protection be realized during the service process but also patients can manage their own medical data autonomously, which is beneficial to privacy protection under the medical data sharing.

Yikang Lin, Peng Zhang

Electronic voting (E-voting) protocol is that voters can vote according to their wishes, and then the voting authority is responsible for collecting the votes and counting the final voting result. With the development of Blockchain, we tend to combine it with E-voting and propose Blockchain-based complete self-tallying E-voting protocol. Its distributed network makes the protocol more available than E-voting protocol based on centralized servers. In our protocol, Blockchain acts as bulletin board, and “Efficient One-out-of-T” zero knowledge proof (ZKP) is proposed to support multi-candidate voting. Moreover, the issues of abortive and adaptive are solved. The security analysis shows that our protocol meets the security requirements of E-voting, and it can be applied to small-scale and anonymous private scenario such as Corporate Board Voting. The performance analysis demonstrates that the proposed ZKP has low time consumption.

Anum Nawaz, Tuan Nguyen Gia, Jorge Peña Queralta, Tomi Westerlund

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.

Zuhaib Akhtar

With the introduction of the term blockchain in 2008, it's interest has been increasing in the community since the idea was coined. The reason for this interest is because it provides anonymity, security and integrity without any central third party organisation in control of data and transaction. It has attracted huge interest in research areas due to its advances in various platforms, limitations and challenges. There are various Distributed Ledger Technologies that demonstrates their special features which overcome limitations of other platforms. However, implementations of various distributed ledger technologies differ substantially based on their data structures, consensus protocol and fault tolerant among others. Due to these variations, they have a quite different cost, performance, latency and security. In this paper, working and in-depth comparison of major distributed ledger technologies including their special features, strengths and weaknesses is presented and discussed by identifying various criteria.

Li Jiang, Shengli Xie, Sabita Maharjan, Yan Zhang

Device to device (D2D) communication is a promising paradigm to support data sharing in the fifth generation (5G) small cell networks. However, D2D communication is more vulnerable to security and privacy threats due to direct wireless connection between the proximity devices. In this paper, we exploit blockchain technology to propose a new distributed and secure data sharing framework named D2D blockchain, where we deploy a set of Access Points (APs) to verify end users' transactions. Besides, in order to enable the end users to receive the required data on time in the D2D blockchain, we propose two new schemes for the transaction confirmation procedure including a relay-assisted transaction relaying scheme and a Delegated Proof-of-Stake (DPoS) based lightweight block verification scheme. Furthermore, to incentivize the relay devices to help in relaying transactions in congested areas, and to encourage the verifiers to contribute their resources in block verification, we design a two-stage contract theory based joint optimization scheme. To measure the quality of the relayed transaction and the verified block, we formulate two models: value of transaction relaying (VoTR) and value of block verification (VoBV), and then design the optimal contract for each relay device and each verifier. Security analysis and numerical results illustrate that the proposed D2D blockchain and the designed joint optimization scheme are secure and efficient for data sharing among end users.

Mathieu Galtier, Camille Marini

Machine learning is promising, but it often needs to process vast amounts of\nsensitive data which raises concerns about privacy. In this white-paper, we\nintroduce Substra, a distributed framework for privacy-preserving, traceable\nand collaborative Machine Learning. Substra gathers data providers and\nalgorithm designers into a network of nodes that can train models on demand but\nunder advanced permission regimes. To guarantee data privacy, Substra\nimplements distributed learning: the data never leave their nodes; only\nalgorithms, predictive models and non-sensitive metadata are exchanged on the\nnetwork. The computations are orchestrated by a Distributed Ledger Technology\nwhich guarantees traceability and authenticity of information without needing\nto trust a third party. Although originally developed for Healthcare\napplications, Substra is not data, algorithm or programming language specific.\nIt supports many types of computation plans including parallel computation plan\ncommonly used in Federated Learning. With appropriate guidelines, it can be\ndeployed for numerous Machine Learning use-cases with data or algorithm\nproviders where trust is limited.\n

Yuling Chen, Jinyi Guo, Changlou Li, Wei Ren

In the big data era, data are envisioned as critical resources with various values, e.g., business intelligence, management efficiency, and financial evaluations. Data sharing is always mandatory for value exchanges and profit promotion. Currently, certain big data markets have been created for facilitating data dissemination and coordinating data transaction, but we have to assume that such centralized management of data sharing must be trustworthy for data privacy and sharing fairness, which very likely imposes limitations such as joining admission, sharing efficiency, and extra costly commissions. To avoid these weaknesses, in this paper, we propose a blockchain-based fair data exchange scheme, called FaDe. FaDe can enable de-centralized data sharing in an autonomous manner, especially guaranteeing trade fairness, sharing efficiency, data privacy, and exchanging automation. A fairness protocol based on bit commitment is proposed. An algorithm based on blockchain script architecture for a smart contract, e.g., by a bitcoin virtual machine, is also proposed and implemented. Extensive analysis justifies that the proposed scheme can guarantee data exchanging without a trusted third party fairly, efficiently, and automatically.

Bo Yin, Yulei Wu, Tianshi Hu, Jiaqing Dong · 5 authors

With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)-a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.

Fran Casino, Constantinos Patsakis

Information overload is a phenomenon of our days due to the unprecedented penetration of information and communication technologies (ICT) in our daily lives. As a result, people often end up with more options than they can process to choose from and therefore may opt for choices which do not fit best to their preferences. To address these issues, recommender systems (RSs) were proposed and have gained a lot of interest from the research community and industry. However, privacy is a big concern in these systems. While decentralized recommenders can protect privacy, they lack the needed efficiency to be widely adopted. In this article, we use blockchain as the backbone of a decentralized RS, managing to equip it with a broad set of features while simultaneously, preserving user's privacy. We introduce a new architecture, based on decentralized locality sensitive hashing classification as well as a set of recommendation methods, according to how data are managed by users. Extensive experimental results illustrate the performance and efficacy of our approach compared with state-of-the-art methods. In addition, a discussion about its benefits and opportunities provides ground for further research.

Tanzeela Sultana, Abdul Ghaffar, Muhammad Azeem, Zain Abubaker · 6 authors

No abstract is available for this record.

Abdul Ghaffar, Muhammad Azeem, Zain Abubaker, Muhammad Usman Gurmani · 7 authors

No abstract is available for this record.

Omaji Samuel, Nadeem Javaid, Faisal Shehzad, Muhammad Sohaib Iftikhar · 7 authors

No abstract is available for this record.

Konstantinos Rantos, George Drosatos, Antonios Kritsas, Christos Ilioudis · 6 authors

In the Internet of Things (IoT) ecosystem, the volume of data generated by devices in the user’s environment is continually increasing and becoming of particular value. In such an environment the average user is bound to face considerable difficulties in understanding the size and scope of his/her collected data. However, the provisions of the European General Data Protection Regulation (GDPR) require data subjects to be able to control their personal data, be informed, and consent to its processing in an intelligible manner. This paper proposes ADVOCATE platform, a user-centric solution that allows data subjects to easily manage consents regarding access to their personal data in the IoT ecosystem. The proposed platform also assists data controllers to meet GDPR requirements, such as informing data subjects in a transparent and unambiguous manner about the data they will manage, the processing purposes, and periods. The integrity of personal data processing consents and the immutable versioning control of them are protected by a blockchain infrastructure. Finally, the paper provides a prototype implementation of the proposed platform that supports the main consents management functionality.

Keke Gai, Yulu Wu, Liehuang Zhu, Zijian Zhang · 5 authors

Contemporarily, two emerging techniques, blockchain and edge computing, are driving a dramatical rapid growth in the field of Internet-of-Things (IoT). Benefits of applying edge computing is an adoptable complementarity for cloud computing; blockchain is an alternative for constructing transparent secure environment for data storage/governance. Instead of using these two techniques independently, in this article, we propose a novel approach that integrates IoT with edge computing and blockchain, which is called blockchain-based Internet of Edge model. The proposed model, designed for a scalable and controllable IoT system, sufficiently exploits advantages of edge computing and blockchain to establish a privacy-preserving mechanism while considering other constraints, such as energy cost. We implement experiment evaluations running on Ethereum. According to our data collections, the proposed model improves privacy protections without lowering down the performance in an energy-efficient manner.

Iago Sestrem Ochôa, Luís Augusto Silva, Gabriel de Mello, Bruno Alves da Silva · 8 authors

With the popularization of the Internet-of-Things, various applications have emerged to make life easier. These applications generate a large amount of user data. Analyzing the data obtained from these applications, one can infer personal information about each user. Considering this, it is clear that ensuring privacy in this type of application is essential. To guarantee privacy various solutions exist, one of them is UbiPri middleware. This paper presents a decentralized implementation of UbiPri middleware using the Ethereum blockchain. Smart contracts were used in conjunction with a communication gateway and a distributed storage service to ensure users privacy. The results obtained show that the implementation of this work ensures privacy at different levels, data storage security, and performance regarding scalability in the Internet of Things environments.

Jonathan Passerat‐Palmbach, Tyler Farnan, Robert Miller, Marielle S. Gross · 6 authors

We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs events in the network without revealing identities.

Wuhui Chen, Yufei Chen, Xu Chen, Zibin Zheng

Currently, data sharing for the Internet of Vehicles (IoV) applications has drawn much attention in the framework of developing smart cities and smart transportation. A critical challenge for data sharing is to incentivize users to participate in collecting and sharing data. The traditional incentive mechanism of crowdsourcing is not practical for IoV because of its trust issues. Although blockchain technology has been introduced to address trust issues and security challenges, ensuring trust in off-chain data for the blockchain-based approaches is still an open issue. In this article, we propose a quality-driven auction-based incentive mechanism based on a consortium blockchain that guarantees trust in both on-chain data and off-chain data. We first introduce a consortium blockchain that is used as an open and distributed hyperledger to address the security issue of on-chain data. Then, we formulate the problem as a reverse auction in which the platform acts as an auctioneer that purchases data from users. By utilizing a data quality-driven auction model, the evaluated data quality via expectation maximization is used to ensure the trust in off-chain data. The quality-driven, auction-based incentive mechanism can obtain the high-quality data and optimal social welfare with low social cost. Otherwise, we design a smart contract to perform the data sharing automatically. Finally, the extensive simulations show that our proposed algorithm achieves maximum social welfare, outperforms other solutions, and scales well when the number of users or tasks increase. Moreover, the performance of the smart contract shows its low computing cost.