Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.
By training a machine learning algorithm across multiple decentralized edge nodes, federated learning (FL) ensures the privacy of the data generated by the massive Internet-of-Things (IoT) devices. To economically encourage the participation of heterogeneous edge nodes, a transparent and decentralized trading platform is needed to establish a fair market among distinct edge companies. In this article, we propose a hybrid blockchain-based resource trading system that combines the advantages of both public and consortium blockchains. We design and implement a smart contract to facilitate an automatic, autonomous, and auditable rational reverse auction mechanism among edge nodes. Moreover, we leverage the payment channel technique to enable credible, fast, low-cost, and high-frequency payment transactions between requesters and edge nodes. Simulation results show that the proposed reverse auction mechanism can achieve the properties, including budget feasibility, truthfulness, and computational efficiency.
In this demo, we demonstrate a novel blockchain based protocol for federated learning. We present the system architecture and describe the blockchain based protocol that seamlessly provides secure communication in federated learning with physically distributed data sets.
A Large-Scale Heterogeneous Network (LS-HetNet) integrates different networks into one uniform network system to provide seamless one-world network coverage. In LS-HetNet, various devices use different technologies to access heterogeneous networks and generate a large amount of data. For dealing with a large number of access requirements, these data are usually stored in the HetNet Domain Management Server (HDMS) of the current domain, and HDMS uses a centralized Authentication/Authorization/Auditing (AAA) scheme to protect the data. However, this centralized method easily causes the data to be modified or disclosed. To address this issue, we propose a blockchain-empowered AAA scheme for accessing data of LS-HetNet. Firstly, the account address of the blockchain is used as the identity authentication, and the access control permission of data is redesigned and stored on the blockchain, then processes of AAA are redefined. Finally, the experimental model on Ethereum private chain is built, and the results show that the scheme is not only secure but also decentral, without tampering and trustworthiness.
Digital identity is the key element of digital transformation in representing any real-world entity in the digital form. To ensure a successful digital future the requirement for an effective digital identity is paramount, especially as demand increases for digital services. Several Identity Management (IDM) systems are developed to cope with identity effectively, nonetheless, existing IDM systems have some limitations corresponding to identity and its management such as sovereignty, storage and access control, security, privacy and safeguarding, all of which require further improvement. Self-Sovereign Identity (SSI) is an emerging IDM system which incorporates several required features to ensure that identity is sovereign, secure, reliable and generic. It is an evolving IDM system, thus it is essential to analyse its various features to determine its effectiveness in coping with the dynamic requirements of identity and its current challenges. This paper proposes numerous governing principles of SSI to analyse any SSI ecosystem and its effectiveness. Later, based on the proposed governing principles of SSI, it performs a comparative analysis of the two most popular SSI ecosystems uPort and Sovrin to present their effectiveness and limitations.
Adam Ibrahim Abdi, Fathy Eassa, Kamal Jambi, Khalid Ali Almarhabi · 5 authors
The Internet of Things paradigm is growing rapidly. In fact, controlling this massive growth of IoT globally raises new security and privacy issues. The traditional access control mechanisms provide security to IoT systems such as DAC (discretionary access control) and mandatory access control (MAC). However, these mechanisms are based on central authority management, which raises some issues such as absence of scalability, single point of failure, and lack of privacy. Recently, the decentralized and immutable nature of blockchain technology integrated with access control can help to overcome privacy and security issues in the IoT. This paper presents a review of different access control mechanisms in IoT systems. We present a comparison table of reviewed access control mechanisms. The mechanisms’ scalability, distribution, security, user-centric, privacy and policy enforcing are compared. In addition, we provide access control classifications. Finally, we highlight challenges and future research directions in developing decentralized access control mechanisms for IoT systems.
Blockchain technology has triggered a new round of technological innovation and industrial change. Promoting the deep integration of blockchain technology and smart water conservancy has become an essential part of the digital transformation of the Chinese smart water conservancy industry. On the basis of the analysis of the framework of the smart water conservancy system and the technology infrastructure model of the blockchain, a conceptual model of a smart water conservancy information sharing platform is established. The platform has four-in-one and multicentralized participants including government departments, water conservancy enterprises, the public, and third-party maintenance, and the design of “alliance chain + private chain” dual chain smart contract. Moreover, the multidimensional scenarios of smart water conservancy innovation applications incorporating blockchain platform technology are addressed. Then, the water rights trading market is taken as an example to propose an innovative development path with consensus mechanisms, smart contracts, asymmetric encryption, and information source tracing. Although there may be some questions and challenges related to smart water conservancy and blockchain technology, this paper can provide a reference for the sustainable development of the smart water conservancy industry due to its fundamental societal contributions.
Omi Akter, Arnisha Akther, Md. Ashraf Uddin, Md. Manowarul Islam
With the advancement in digital forensics, digital forensics has been evolved in Cloud computing.A common process of digital forensics mainly includes five steps: defining problem scenario, collection of the related data, investigation of the crime scenes, analysis of evidences and case documentation.The conduction of digital forensics in cloud results in several challenges, security, and privacy issues.In this paper, several digital forensics approaches in the context of IoT and cloud have been presented.The review focused on zone-based approach for IoT digital forensics where the forensics process is divided into three zones.Digital forensics in cloud provides the facilities of large data storage, computational capabilities and identification of criminal activities required for investigating forensics.We have presented a brief study on several issues and challenges raised in each phase of Cloud forensics process.The solution approaches as well as advancement prospects of cloud forensics have been described in the light of Blockchain technology.These studies will broaden the way to new researchers for better understanding and devising new ideas for combating the challenges.
Qikun Zhang, Yongjiao Li, Ruifang Wang, Lu Liu · 6 authors
With the widespread application of Industrial Internet of Things (IIoT) technology in the industry, the security threats are also increasing. To ensure the safe sharing of resources in IIoT, this paper proposes a data security sharing model based on privacy protection (DSS-PP) for blockchain-enabled IIoT. Compared with previous works, DSS-PP has obvious advantages in several important aspects: (1) In the process of identity authentication, it protects users' personal information by using authentication technology with hidden attributes; (2) the encrypted shared resources are stored in off-chain database of the blockchain, while only the ciphertext index information is stored in the block. It reduces the storage load of the blockchain; (3) it uses blockchain logging technology to trace and account for illegal access. Under the hardness assumption of Inverse Computational Diffe–Hellman (ICDH) problem, this model is proven to be correct and safe. Through the analysis of performance, DSS-PP has better performance than the referred works.
A boardroom election is an election with a small number of voters carried out with public communications. We present BVOT, a self-tallying boardroom voting protocol with ballot secrecy, fairness (no tally information is available before the polls close), and dispute-freeness (voters can observe that all voters correctly followed the protocol). BVOT works by using a multiparty threshold homomorphic encryption system in which each candidate is associated with a masked unique prime. Each voter engages in an oblivious transfer with an untrusted distributor: the voter selects the index of a prime associated with a candidate and receives the selected prime in masked form. The voter then casts their vote by encrypting their masked prime and broadcasting it to everyone. The distributor does not learn the voter's choice, and no one learns the mapping between primes and candidates until the audit phase. By hiding the mapping between primes and candidates, BVOT provides voters with insufficient information to carry out effective cheating. The threshold feature prevents anyone from computing any partial tally---until everyone has voted. Multiplying all votes, their decryption shares, and the unmasking factor yields a product of the primes each raised to the number of votes received. In contrast to some existing boardroom voting protocols, BVOT does not rely on any zero-knowledge proof; instead, it uses oblivious transfer to assure ballot secrecy and correct vote casting. Also, BVOT can handle multiple candidates in one election. BVOT prevents cheating by hiding crucial information: an attempt to increase the tally of one candidate might increase the tally of another candidate. After all votes are cast, any party can tally the votes.
Data sharing is a vital method for data intensive and cooperative work-based applications. A central database system, which collects and stores data generated from different parties and provides open access, can not only save costs on infrastructures, but also provide consistent and uptodate data for all the participants. However, privacy and security issues also emerge especially when the shared data is highly sensitive, such as electronic medical records (EMRs). In this paper, we propose a new data sharing scheme with access control based on CP-ABE, searchable encryption, and blockchain. We realize two main goals in our scheme. First, data owners can share their data with multiple users who satisfy the access policy rather than just a single user. Second, the difficulty of the ciphertext query is overcome by means of searchable encryption technology. Most importantly, data owners do not need to interact with the data querier in the process of sharing. Both theoretical analysis and practical experiments show that the proposed scheme is secure and efficient.
Privacy plays a central role in many application domains that utilize blockchain technology. It is central in Personal Data Management, Electronic Health records, or systems that interact with any public institution. However, blockchains are subject to potential privacy issues such as transaction linkability, compliance with data protection regulations, on-chain data privacy, and malicious smart contracts. To deal with these challenges, novel privacy-preserving solutions based on crypto-privacy techniques are emerging. The goal of this survey is to provide insights into the privacy-preserving techniques associated with blockchain. We analyse the existing privacy-preserving mechanisms for blockchain and propose a framework that categorizes the main techniques examined. Furthermore, we summarize some typical applications of blockchain where the privacy protection is the main requirement.
In the medical system, the verification, preservation and synchronization of electronic medical records has always been a difficult problem, and the random dissemination of patient records will bring various risks to patient privacy. Therefore, how to achieve secure data sharing on the basis of ensuring users' personal privacy becomes the key. In recent years, blockchain has been proposed to be a promising solution to achieve data sharing with security and privacy preservation due to its advantages of immutability. So, a distributed electronic medical records searchable scheme was proposed by leveraging blockchain and smart contract technology. Firstly, we perform a hash calculation on the electronic medical data and store the corresponding value on the blockchain to ensure its integrity and authenticity. Then, we encrypt the electronic medical data and store it in the interplanetary file system which is a distributed storage protocol. These operations not only can solve centralized data store of servers of several medical institutions, but also be good at lowering stress from data store and high-frequency access to blockchain. Secondly, the encrypted keyword index information of electronic medical records was stored on the Ethereum blockchain, meanwhile a smart contract deployed in the Ethereum blockchain is used to realize keyword search instead of depending on a centralized third party. Furthermore, we use attribute-based encryption scheme to ensure that only the attributes meeting the access policy can decrypt the encrypted electronic medical records. Finally, our performance analysis and security analysis show that the scheme is secure and efficient.
Data stored on centralized cloud servers may have some risks. Moreover, it may leak the data access pattern when accessing data on cloud servers. Oblivious RAM (ORAM) is a candidate solution to hide the data access pattern, but it inherently induces some overheads of accessing data. In this paper, we address the issues above, and propose the oblivious access system on decentralized database over parallel smart contract model. The proposed system can slightly reduce the overhead of ORAM and overcome the problem from centralized servers. The main techniques are to use parallel smart contract model to improve the performance of smart contract execution and garbled circuit to reduce the cost of communication.
Mahdi Daghmehchi Firoozjaei, Rongxing Lu, Ali A. Ghorbani
Abstract Blockchain‐based applications provide many promising opportunities to overcome the challenges associated with the Internet of Things (IoT) ecosystems (eg, centralized architecture, data integrity, and reliability). In particular, blockchain technology offers many desirable features for IoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. However, while logging all transactions in a distributed blockchain ledger provides transparency, it also makes it possible to compromise user's privacy, thus posing a grand challenge to IoT architects and implementers. Over the past years, a set of solutions have been proposed for various scenarios, to address these privacy issues. In this paper, we survey these solutions, classify, and analyze their advantages and disadvantages. We also introduce an evaluation framework to evaluate the quality of the privacy‐preserving based on an adjustable weighting scheme. Finally, we rate the analyzed solutions based on their privacy ranks, and hope our evaluation can shed light on the future design of privacy‐preserving solutions applicable for blockchain‐based IoT platforms.
This study proposes a blockchain-based copyright depository scheme. Blockchain is used as an underlying architecture to develop the digital copyright depository system. The system is built to solve the existing problems of the copyright depository, such as ambiguous ownership, low cost of piracy, rampant piracy, and difficulty in copyright monitoring, protection and sharing. Digital verification technology, supervision nodes, Oracle service and smart contract are introduced to provide certification depository, flow tracking, rights protection and sharing services for digital content. A system performance test is carried out for the registration service and query service, and the results show that the throughput of the two services was 195 and 366 times/second respectively with zero failure rate. The blockchain-based copyright depository system has been developed into a software as a service (SaaS) platform-blockchain copyright depository platform and provides services for dozens of institutions and enterprises, such as Zhijiang Notary Office, Xinhua Zhiyun, Himalaya, etc.
Sadaf MD Halim, Latifur Khan, Bhavani Thuraisingham
Mobile devices are a rich source of sensitive location data. In this paper, we propose a method for harnessing this data to provide better location predictions without sacrificing the privacy of the users generating this data. To this end, we propose utilizing Federated Learning to train locally on a user's mobile device, while simultaneously identifying and combatting the possibility of bad actors or adversaries that may deliberately report problematic data to hurt the training process. Furthermore, we propose using a blockchain instead of a centralized server for the training process, to ensure that the process is secure.
Elizabeth C. Crites, Mary Maller, Sarah Meiklejohn, Rebekah Mercer
Token-curated registries (TCRs) are a mechanism by which a set of users are able to jointly curate a reputable list about real-world information. Entries in the registry may have any form, so this primitive has been proposed for use—and deployed—in a variety of decentralized applications, ranging from the simple joint creation of lists to helping to prevent the spread of misinformation online. Despite this interest, the security of this primitive is not well understood, and indeed existing constructions do not achieve strong or provable notions of security or privacy. In this paper, we provide a formal cryptographic treatment of TCRs as well as a construction that provably hides the votes cast by individual curators. Along the way, we provide a model and proof of security for an underlying voting scheme, which may be of independent interest. We also demonstrate, via an implementation and evaluation, that our construction is practical enough to be deployed even on a constrained decentralized platform like Ethereum.
Blockchain technologies have been very effective in processing distributed transactions securely. They have many applications including in handling bitcoin cryptocurrencies and smart contracts. More recently the use of blockchain has been explored for data science applications. This paper examines blockchain technologies and discusses their applications in data science and cyber security.
Secure identity management is one of the controversial topics in the cybersecurity landscape. The user needs to rely on third-party providers for managing their personal information, which is a big security concern. Traditional identity systems typically suffer from single points of failure, lack of interoperability, and privacy issues such as encouraging mass data collection and user tracking. Blockchain technology has the potential to support novel data ownership and governance models with built-in control and consent mechanisms, which may benefit both users and businesses by alleviating these concerns. Blockchain-based systems provide the possibility for their users to insert their data in this distributed ledger. Users can trust the blockchain as it is leveraging consensus mechanisms to validate and gather the transactions in immutable blocks. The transparent and decentralized nature of the blockchain network enables the development of a non-refutable and unbreakable record of data, which is the fundamental feature of many applications, such as identity management.