Blockchain-Based Fair Research Articles

BFFDT: Blockchain-based Fair and Fine-Grained Data Trading Using Proxy Re-Encryption and Verifiable Commitment

BFFDT: Blockchain-based Fair and Fine-Grained Data Trading Using Proxy Re-Encryption and Verifiable Commitment

Blockchain-Based Fair and Fine-Grained Data Trading With Privacy Preservation

In this paper, we propose a blockchain-based fair and privacy-preserving data trading scheme that supports fine-grained data selling. First, to achieve fairness for trading participants, by incorporating attribute-based credentials, encryption, and zero-knowledge proof, we design a data trading scheme where a buyer first publishes the required data attributes on the blockchain, and a data seller can demonstrate data availability in ciphertext by only disclosing the required attributes to a data buyer and proving the authenticity of data. A data buyer transfers funds only if the correct key material is uploaded to the blockchain. Second, to guarantee fine-grained data trading and preserve identity privacy, we build a Merkle hash tree on the ciphertexts of data with a signature on its root node, which allows a data seller to split data into blocks and remove the sensitive information from the data without affecting data availability verification. The public key of the data seller is not leaked to the data buyer during the trading. Moreover, different trading transactions from the same data seller cannot be linked. We formally prove that our scheme achieves the desired security properties: fairness and privacy preservation. Simulation results demonstrate the feasibility and efficiency of the proposed scheme.

Blockchain-Based Fair Payment for ABE with Outsourced Decryption

Blockchain-Based Fair Payment for ABE with Outsourced Decryption

Blockchain-Based Gas Auctioning Coupled with a Novel Economic Dispatch Formulation for Gas-Deficient Thermal Plants

Inadequate gas supply is partly responsible for the energy shortfall experienced in some energy-poor nations. Favorable market conditions would boost investment in the gas supply sector; hence, we propose a blockchain-based fair, transparent, and secure gas trading scheme that facilitates peer-to-peer trading of gas. The scheme is developed using an Ethereum-based smart contract that receives offers from gas suppliers and bid(s) from the thermal plant operator. Giving priority to the cheapest offers, the smart contract determines the winning suppliers. This paper also proposes an economic dispatch model for gas-deficient plants. Conventional economic dispatch seeks to satisfy electric load demand whilst minimizing the total gas cost of generating units. Implicit in its formulation is the assumption that gas supply to generating units is sufficient to satisfy available demand. In energy poor nations, this is hardly the case as there is often inadequate gas supply and conventional economic dispatch is of little practical value. The proposed economic dispatch model’s objective function maximizes the quantity of available gas and determines the optimal power output of each generating unit. The mathematical formulation is verified using data from the Egbin thermal station which is the largest thermal station in Nigeria and is solved using the General Algebraic Modeling System (GAMS). Obtained results indicate the viability of the novel approach as it results in a net power gain of 35 MW. On the other hand, the smart contract proved effective in accurately selecting winning suppliers and making payment.

FGFL: A blockchain-based fair incentive governor for Federated Learning

Federated Learning is a framework that coordinates a large amount of workers to train a shared model in a distributed manner, in which the training data are located on the workers' sides in order to preserve data privacy. There are two challenges in the crowdsourcing of FL, the workers who participant in training need to consume computing and communication resources, so that they are reluctant to participate in the training process if they can not get reasonable rewards. Moreover, there may be attackers who send arbitrary updates to get undeserving compensation or even destroy the model, thus, effective prevention of malicious workers is also critical. An incentive mechanism is urgently required in order to encourage high-quality workers to participate in FL and to punish the attackers. In this paper, we propose FGFL, a blockchain-based incentive governor for Federated Learning. In FGFL, we assess the participants with reputation and contribution indicators. Then the task publisher rewards workers fairly to attract efficient ones while the malicious ones are punished and eliminated. In addition, we propose a blockchain-based incentive management system to manage the incentive mechanism. We evaluate the effectiveness and fairness of FGFL through theoretical analysis and comprehensive experiments. The evaluation results show that FGFL fairly rewards workers according to their corresponding behavior and quality. FGFL increases the system revenue by 0.2% to 3.4% in reliable federations compared with baselines. And in the unreliable scenario where contains attackers, the system revenue of FGFL outperforms the baselines by more than 46.7%.

Blockchain-based Fair and Decentralized Data Trading Model

Abstract Data is a kind of important asset in the digital economy and is driving the rise of data markets. Meanwhile, data markets promote data trading efficiently and improve the utilization of data. However, several challenges about data trading need to be addressed. Here, we resolve these challenges via our blockchain-based fair and decentralized data trading model. Disputes about data correctness is settled by the decentralized arbitration mechanism in our model. To ensure the fairness of data trading, we integrate a sale contract and a deterministic public-key encryption algorithm. The decentralization feature of blockchain cuts off the single-point failure for the data trading platform. In addition, we prove that the proposed protocol achieves the desirable security properties that a secure data trading protocol should have. Moreover, utilizing the smart contract in Solidity and program in Java, we implement our model and then evaluate its performance.

Blockchain-Based Fair and Secure Certified Electronic Mail Without a TTP

Certified mail is a special postal service that provides the sender with a proof that the mail was successfully received by the other party. However, when it comes to certified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electronic</i> mail (CEM), there has not been a widely accepted protocol yet. Most of the existing solutions rely on a Trusted Third Party (TTP) in order to achieve fairness, which can become a bottleneck and lead to more charges and computations. Other solutions that do not rely on a TTP have a relatively high computation and communication overhead. Blockchains have allowed the creation of TTP-free protocols for fair exchange; however, the existing Blockchain-based protocols are either too expensive or do not meet the necessary fairness and confidentiality guarantees. In this work, we have developed two TTP-free Blockchain-based protocols for certified electronic mail that achieve the required security properties of strong fairness, non-repudiation, timeliness, and confidentiality. The first protocol uses simple bitcoin transactions for a single receiver, whereas the second one implements smart contracts for one or multiple receivers. We have analyzed and compared both protocols to existing TTP-free Blockchain solutions and have demonstrated their superior properties and cost. Furthermore, we have implemented the two protocols in a realistic testbed, demonstrating the viability of our approach.

OBFP: Optimized Blockchain-Based Fair Payment for Outsourcing Computations in Cloud Computing

Outsourcing computations have been widely used to meet the growing computing demands, although achieving trust in an untrusted (or a zero-trust) environment can be challenging in practice. Fair payment, a candidate solution, can potentially facilitate fair trading among outsourcing computation participants such as users and workers. However, most existing solutions including traditional e-cash-based or blockchain-based, may potentially compromise the worker’s fairness (i.e., does not achieve robust fairness, since trusted third parties are required during the trading process), or involve heavy zero-knowledge proofs (ZKPs, with significant computation costs). To mitigate these limitations, we propose a system model of an optimized blockchain-based fair payment (OBFP) for outsourcing computations. Then, we construct a ZKP-free solution based on blockchain by combining any secure commitment, accumulator, and symmetric encryption schemes, as well as a hash function. To demonstrate the utility of our proposed OBFP system, we provide security analysis, performance evaluation and a comparison with existing popular solutions. Specifically, the cryptographic tools are instantiated as commitment (Perdesen commitment), accumulator (RSA-based accumulator), and symmetric encryption (a concrete scheme with the indistinguishability under chosen-plaintext attack (IND-CPA) security), and a hash function (Keccak-256). The prototype is implemented in COSBench and Remix to analyze cloud scalability and concurrency, as well as gas cost.

The Genesy Model for a Blockchain-Based Fair Ecosystem of Genomic Data

Recent advances in technology have drastically downsized costs and implementation times for genomic services. The wide availability of low-cost genomic technologies and easy access to genomic data can significantly improve healthcare productivity and efficiency, all to the benefit of social well-being in general. For example, by creating the conditions for researchers to identify the causes of multiple diseases and contributing to the development of new drugs, we can improve the quality of life and give people, as users of genomic services, the means to positively impact their health. This article describes how blockchain technology can lay the foundations of an ecosystem that encourages users to acquire and share their genomic data in full awareness without fear of being circumvented to participate in the benefits and advances in genomic research. The starting point is Genesy, an innovative blockchain platform that transcribes genomic data, thus facilitating and, at the same time safeguarding, the users of genomic services in their relationships with parties interested in accessing and using data they own, such as research centers, pharmaceutical companies, hospitals, and geneticists. This result is obtained by exploiting blockchain technology's capabilities to notarize data and prevent their unauthorized use, and at the same time to make them objects of possible transactions between different parties. Looking ahead, the Genesy model can be generalized to promote an ecosystem, and a fair market, for all types of biomedical data.

Blockchain-Based Fair and Secure Electronic Double Auction Protocol

Double auction is an auction in which multiple buyers and sellers looking for a price where supply and demand balance. Since the electronic double auction based on secure multiparty computation (DABSMPC) cannot guarantee its fairness, we propose a blockchain-based fair and secure electronic double auction protocol (BFSDA). BFSDA modifies the data input and distribution mechanism of participants in DABSMPC, which improves the security of the protocol. Then, the BFSDA introduces and improves the blockchain-based fair and secure multiparty computation protocol (BFSMPC) to ensure fairness while increasing the success rate of secret recovery. In addition, BFSDA uses a fairer and more efficient protocol for secure two-party comparing to obtain the final marketing clearing price. The schema analysis result of BFSDA shows that: first, the private input data will not be revealed as long as data owner is not compromised, second, honest participants can get the result or economic compensation, and third, participants only need to pay the deposit once and a large amount of complicated verification operations are carried out off the chain, which ensures the efficiency of the protocol.

Toward Blockchain-Based Fair and Anonymous Ad Dissemination in Vehicular Networks

Commercial advertisement (ad) dissemination has been proliferating on connected vehicles, allowing users to promote their products via vehicle-to-vehicle/-infrastructure communications. Despite the prospect of ad dissemination in vehicular networks, it faces challenges upon deployment especially on security and privacy. Particularly, vehicles may collude to defraud the advertiser to obtain rewards without disseminating ads, which may cause unfair “free-riding” issue in these activities. Furthermore, concerns on possible privacy leakage may discourage vehicles to participate in the process of ad dissemination. In addition, external DDoS attacks and internal single point of failure may also affect service availability. To address these issues, we explore the potential of blockchain technology to construct a fair and anonymous scheme for advertising in vehicular networks. We first present the overview of the blockchain-based ad dissemination framework. Then, under the framework, we design a concrete, fair and anonymous scheme. To ensure fairness, we utilize the Merkle hash tree together with smart contracts to achieve the “proof-of-ad-receiving” property (i.e., check whether a vehicle indeed receives an ad without deception or introducing significant storage cost) to mitigate the “free-riding” attack. On the other hand, any ad receiver who acquires a dissemination reward per ad more than once can be effectively detected and will be punished which is achieved by using smart contracts. Additionally, the proposed scheme can protect vehicles’ privacy in terms of anonymity and conditional linkability based on zero-knowledge proof techniques. Lastly, extensive security analysis and implementations demonstrate the feasibility and efficiency of the scheme.

FaDe: A Blockchain-Based Fair Data Exchange Scheme for Big Data Sharing

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.

BFR-MPC: A Blockchain-Based Fair and Robust Multi-Party Computation Scheme

In a general secure multi-party computation (MPC) protocol, two or more participants who do not trust each other, use their respective secret inputs to calculate a joint function in a distributed environment without a centralized organization. They can get correct outputs on the premise of ensuring privacy and independence of input. In this paper, to solve the problem of fairness and robustness in MPC, a blockchain-based multi-party computation scheme (BFR-MPC) was proposed. The blockchain maintains an open reputation system for parties as a public ledger where a more reputable party has a greater chance to be selected. The block height is used as a trusted timestamp. In each round, parties must send the correct information before the deadline. In our scheme, all parties are considered to be foresighted, and an incentive mechanism encourages parties to cooperate rather than deviate from the protocol. Because of non-cooperative parties will be immediately expelled from the protocol and will be penalized financially, the proposed scheme is robust. The penalty will be used to reward honest parties. We also proved the fairness of our scheme through Game Theory. The comparison results of the proposed scheme with other schemes show that it is a more practical scheme for MPC with high fairness and robustness.

Blockchain-Based Fair Payment Protocol for Deduplication Cloud Storage System

Today more and more enterprises and individuals are outsourcing their data to cloud storage system. Data deduplication is one of the important technologies to reduce the storage cost of cloud storage system. In a cloud storage system with deduplication technology, the client can outsource the data files to the cloud storage server and pay for them. Fair payment is one of key issues in the cloud deduplication storage system. At present, a variety of secure deduplication encryption schemes have been designed to protect the privacy of client data. However, most existing fair payment solutions use traditional electronic cash systems to generate payment tokens, which requires a trusted authority to prevent double-spending. Trusted authorities will become bottlenecks in the payment system. Faced with this problem, in this paper, we propose a new decentralized fair payment protocol for cloud deduplication storage system by utilizing ethereum blockchain technology. The new protocol takes advantage of the decentralization of blockchain technology, allowing direct transactions without the participation of trusted third parties. In the new protocol, if a malicious situation occurs, the system can guarantee fair payment by pre-storing penalty money in the smart contract. Safety analysis and experimental analysis show that our new protocol is feasible.

Secure Pub-Sub: Blockchain-Based Fair Payment With Reputation for Reliable Cyber Physical Systems

The cyber physical system (CPS) has gained considerable success in large-scale distributed integration environment. In such systems, the sensor devices collect data which would be disseminated via reliable manner to all interested co-operant entities from the physical world. However, highly unreliable environment of CPS, for example, a number of limitations of existing network middle wares, makes secure and reliable data distribution services a challenge issue. In this paper, we propose a new architecture called secure pub-sub (SPS) without middle ware, i.e., blockchain-based fair payment with reputation. In SPS, publishers publish a topic on the blockchain and subscribers specify an interest message by making a deposit to subscribing the topic. Then, if the interest message matches the topic, the publisher transmits the encrypted content of the topic to the blockchain such that the subscribers can decrypt the ciphertext to obtain the content, and mark the publisher as its reputation. Finally, the publisher receives the payment from the subscriber. The new proposal provides confidentiality and reliability of data, anonymity of subscribers and payment fairness between the publishers and subscribers. Different from the traditional pub-sub services, no trusted third party is involved in our system due to employing blockchain technique. The security of the proposed SPS is analyzed as well. The implementation of the protocol on Ethereum of smart contract demonstrates the validity of SPS.