The current medical cyber physical systems involve a wide range of institutions and a large number of participants. Data sharing among distributed medical institutions is already a development trend. However, the security is worrying; e.g., the access to medical data lacks uniformity and standardization. What is more, data is easy to be tampered with and leaked. This has a very negative impact on the medical industry. Therefore, a strict and reliable access control mechanism for data in the medical cyber physical systems is a prerequisite for ensuring the implementation of modern medical functions. We deal with how to design effective access control in medical cyber physical systems. Combined with blockchain technology, we design the medical cyber physical systems based on blockchain data access control mechanism and unite data in the chain of union Fabric network resources access control. We qualitatively classify medical data, define the weight level of different data, design a medical data access framework based on blockchain, build an applicable model, formulate access control strategy, and specify the role assignment and access task matching of users, so as to achieve secure and effective data access control. The Hyperledger Fabric network is established as the alliance chain for managing access control rights distribution through smart contracts so as to achieve case-based medical data access control under the blockchain.
The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a data trading scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain smart contract and software guard extensions (SGX) to enable owners to fully control users’ identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype comparing with the state-of-the-art schemes with real-world IIoT datasets, which demonstrates the efficiency of DTSI.
With the number of users in crowdsourcing increasing rapidly, task matching service is attracting more and more attention. However, it also causes many security concerns, one of which is the leakage of sensitive information. Privacy-preserving task matching techniques can protect the private information of task requesters and workers. Whereas existing privacy-preserving task matching schemes are constructed on a central server, and thereby they may suffer from potential wrongdoings of a malicious server. In addition, most of them only provide accurate task matching, which means that they cannot tolerate keyword spelling errors, leading to the decline of task matching accuracy. In this paper, we propose a Reliable and Privacy-preserving Task Matching scheme (RPTM) for crowdsourcing. To guarantee the reliability of task matching results, RPTM employs smart contracts to ensure that operations of RPTM are faithfully performed. However, it may still disclose the privacy of users due to the transparency of the blockchain. In order to deal with this problem, RPTM can perform task matching service without compromising the privacy of task requesters and workers by leveraging a novel integer vector encryption scheme. Moreover, RPTM supports multi-keyword fuzzy matching by exploiting locality sensitive hashing and Bloom filter, which can tolerate keyword spelling errors and different expression formats. Extensive analysis and experiments based on a test net of EOS show that RPTM is efficient and secure.
With the integration of the Internet of Things (IoT) in the field of transportation, the Internet of Vehicles (IoV) turned to be a vital method for designing Smart Transportation Systems (STS). STS consist of various interconnected vehicles and transportation infrastructure exposed to cyber intrusion due to the broad usage of software and the initiation of wireless interfaces. This study proposes a federated deep learning-based intrusion detection framework (FED-IDS) to efficiently detect attacks by offloading the learning process from servers to distributed vehicular edge nodes. FED-IDS introduces a context-aware transformer network to learn spatial-temporal representations of vehicular traffic flows necessary for classifying different categories of attacks. Blockchain-managed federated training is presented to enable multiple edge nodes to offer secure, distributed, and reliable training without the need for centralized authority. In the blockchain, miners confirm the distributed local updates from participating vehicles to stop unreliable updates from being deposited on the blockchain. The experiments on two public datasets (i.e., Car-Hacking, TON_IoT) demonstrated the efficiency of FED-IDS against state-of-the-art approaches. It reveals the credibility of securing networks of intelligent transportation systems against cyber-attacks.
Data sharing is very important for accelerating scientific research, business innovations, and for informing individuals. Yet, concerns over data privacy, cost, and lack of secure data-sharing solutions have prevented data owners from sharing data. To overcome these issues, several research works have proposed blockchain-based data-sharing solutions for their ability to add transparency and control to the data-sharing process. Yet, while models for decentralized data sharing exist, how to incentivize these structures to enable data sharing at scale remains largely unexplored. In this paper, we propose incentive mechanisms for decentralized data-sharing platforms. We use smart contracts to automate different payment options between data owners and data requesters. We discuss multiple cost pricing scenarios for data owners to monetize their data. Moreover, we simulate the incentive mechanisms on a blockchain-based data-sharing platform. The evaluation of our simulation indicates that a cost compensation model for the data owner can rapidly cover the cost of data sharing and balance the overall incentives for all the actors in the platform.
Dae-Geun Yoon, Sung-Jin Moon, Kisung Park, Sungkee Noh
As the needs for personal data increase due to the advent of the AI era, many companies are collecting their users' data and using it to advance the service. As the use of personal data increases, the value of personal data also increases. Although these valuable personal data are generated by individuals, only centralized service providers get profit from the data. In this paper, we propose a blockchain-based personal data trading system using DID (Decentralized Identifiers) and VC (Verifiable Credentials). Our proposed system allows users to collect personal data in their own data storage provided by the system. DID and VC are used to authenticate the user's identity and to prove ownership of the data without any centralized systems, respectively. The integrity of the traded data and the history of the transactions are ensured by Hyperledger Fabric, which is a decentralized infrastructure composed of consortium blockchain nodes. We show how our system works by implementing the monitoring system that provides the current status of the user's data and trading. We verify that two end entities including a seller and a buyer can complete personal data trading by using our proposed system without centralized service providers.
Konstantinos Kaltakis, Panagiota Polyzi, George Drosatos, Konstantinos Rantos
Blockchain, a promising technology that has matured and nowadays is widely used in many fields, such as supply chain management, smart grids, agriculture and logistics, has also been proposed for the Internet of Vehicles (IoV) ecosystem to enhance the protection of the data that roadside units and vehicles exchange. Blockchain technology can inherently guarantee the availability, integrity and immutability of data stored in IoV, yet it cannot protect privacy and data confidentiality on its own. As such, solutions that utilise this technology have to consider the adoption of privacy-preserving schemes to address users’ privacy concerns. This paper provides a literature review of proposed solutions that provide different vehicular services using blockchain technology while preserving privacy. In this context, it analyses existing solutions’ main characteristics and properties to provide a comprehensive and critical overview and identifies their contribution in the field. Moreover, it provides suggestions to researchers for future work in the field of privacy-preserving blockchain-enabled solutions for vehicular networks.
Currently many enterprises face issues regarding insufficient data collection samples and data recording dimensions, thus it's hard to make efficient predictions. Since it is limited by the requirement of protecting privacy and trade secrets, data can't be effectively shared among enterprises. Federated learning is an effective method to solve this problem, but there are some performance bottlenecks, information security issues and data trust issues still existed, which need to be improved in combination with other advanced technologies to meet the practical requirements. This paper combines the blockchain technology with federated learning technology, and uses decentralized blockchain system to replace the traditional centralized federated learning architecture. We adopt training method of updating models to achieve machine learning. In this way, we can avoid transmission of intermediate computing data and achieve mechanism of node access, model evaluation, motivation and audit with combination of block chain. In terms of the algorithm, the horizontal federated learning adopts the integrated learning algorithm, and the vertical federated learning adopts the deep learning algorithm. It will be described in detail below.
Jin Sun, Ying Wu, Shangping Wang, Yixue Fu · 5 authors
Federated learning is an emerging technology that solves the privacy problem of training data in multi-party machine learning. However, this technology is vulnerable to a series of system security problems. In this letter, we leverage Hyperledger Fabric permissioned blockchain architecture to build a secure and reliable federated learning platform across multiple data owners, where individual local updates are encrypted based on threshold homomorphic encryption and then recorded on a distributed ledger. The security analysis shows that our solution can effectively deal with the existing privacy and security issues in the federated learning system. The numerical results show that the scheme is feasible and efficient.
In this paper, we present the design and implementation of a blockchain-enabled secure sensing data processing and logging system. Although in our implementation we use the IOTA distributed ledger, the main mechanisms introduced in the system are independent from any particular blockchain platform for maximum flexibility. A blockchain platform is used as an immutable datastore to store critical data for secure sensing data processing and logging. This system corporates several innovative mechanisms. First, a sensor identification mechanism is put in place to ensure that only sensing data submitted by eligible sensors are accepted. Second, only aggregated sensing data are transmitted to the blockchain for safe-keeping. This strategy has two benefits: (1) it reduces the throughput requirement on the public blockchain; and (2) it saves on the transaction fees for asking the blockchain to store the data. Furthermore, a mechanism is introduced to allow two-level logging using a local datastore in conjunction with the blockchain to extend the immutability property offered by the blockchain to the locally stored raw sensing data. Third, a set of mechanisms are designed to facilitate the query of the blockchain for a particular subset of the aggregated data, and to ensure that given an aggregated data item, the corresponding raw data can be quickly located, which is essential for data retrieval needed when situations arise such as forensic analysis in cases of incidents and for auditing purposes. The system is implemented in the Python programming language and a preliminary testing on the functionality of the system has been conducted.
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.
Patrick McCorry, Maryam Mehrnezhad, Ehsan Toreini, Siamak F. Shahandashti · 5 authors
This article discusses secure methods to conduct e-voting over a blockchain in three different settings: decentralized voting, centralized remote voting, and centralized polling station voting. These settings cover almost all voting scenarios that occur in practice. A proof-of-concept implementation for decentralized voting over Ethereum’s blockchain is presented. This work demonstrates the suitable use of a blockchain not just as a public bulletin board but, more importantly, as a trustworthy computing platform that enforces the correct execution of the voting protocol in a publicly verifiable manner. We also discuss scaling up a blockchain-based voting application for national elections. We show that for national-scale elections the major verifiability problems can be addressed without having to depend on any blockchain. However, a blockchain remains a viable option to realize a public bulletin board, which has the advantage of being a “preventive” measure to stop retrospective changes on previously published records as opposed to a “detective” measure like the use of mirror websites. CCS Concepts: • Security and privacy ;
The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things (IoT) systems. Traditional blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent, which however may not always be the truth. As no mechanism ensures the reliability of the original data collected from the IoT devices, these data may be accidently screwed up or maliciously tampered with before they are uploaded on-chain. To address this issue, we propose the Multi-dimensional Certificates of Origin (MCO) method to filter out the potentially incredible data-till all the data uploaded to the chain are credible. To achieve this, we devise the Multi-dimensional Information Cross-Verification (MICV) and Multi-source Data Matching Calculation (MDMC) methods. MICV verifies whether a to-be-uploaded datum is consistent or credible, and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency appears. Large-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.
Blockchain technology is in demand due to its key properties, such as immutability, transparency, distributed storage, and non-centric controls. The election is the primary process for forming a government at local, state, or federal levels in any democratic country. A fair election increases people’s faith in their government. An electronic voting system is a safer solution during this pandemic for the democratic voting system. However, the electronic voting system has a number of potential issues, including security and privacy breaches, the fear of cyber-attacks, the hijacking of the session, and identity theft. Using Blockchain, we can overcome many security and privacy issues as Blockchain uses a cryptographic hash and digital signatures for sending and verifying transactions in the peer-to-peer Blockchain network. Identity management is a critical issue in the election system as many fake voters are available and in the traditional election system, many fake votes are counted. We proposed a system that generates the identity of voters using the tokens and stores them in the interplanetary file system (IPFS). The proposed system provides the storage of tokens and verification of tokens during various stages of polling. The proposed system implemented using the IPFS, as well as the Elliptic Curve Digital Signature Algorithm (ECDSA) for the digital signature, and verification, and SHA-256 as a cryptographic hash. IPFS is used to store data in a distributed manner using the connected nodes in the network. In the proposed system, we use Go IPFS to store the files in Distributed Ledger Technology (DLT). IPFS can store any large file within any format. By splitting the user identity files, we can secure the data and privacy of voters in the Blockchain electronic voting system.
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Abstract With the popularity and development of sensors‐containing intelligent terminals, mobile crowdsensing system (MCS) based on the Internet of Things (IoT) has become a new paradigm of application. By the MCS, the pervasive smart device users are enabled to collect large‐scale data cost‐effectively, for crowd intelligent extraction and human‐centric service delivery. However, most of the existing MCSs are based on a centralized structure vulnerable to attacks and intrusions. Moreover, the data collected through crowdsensing are diverse and difficult to guarantee user privacy, especially during the payment and data upload stages. In this article, we propose a blockchain‐based privacy‐preserving crowdsensing (BPPC) scheme based on the distributed structure, to protect user privacy. First, we combine the multiblockchain technology and K‐anonymity to construct anonymity groups for the confusion. Second, we present the random algorithm HashProof to select candidates from the anonymity groups to avoid deployment of Trusted Third Party (TTP) or agent server. Ultimately, we design encryption‐based algorithms building trust and authentication mechanisms in the system to guarantee the confidentiality of user data and achieve the accurate distribution of rewards. To verify the effectiveness and efficiency of the BPPC scheme, extensive experiments were conducted.
In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, calledBEoTthat has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.
With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.
Jesús García-Rodríguez, Rafael Torres Moreno, Jorge Bernal Bernabé, Antonio Skármeta
Despite the latest efforts to foster the adoption of privacy-enhancing Attribute-Based Credential (p-ABC) systems in electronic services, those systems are not yet broadly adopted. The main reasons behind this are performance efficiency issues, lack of interoperability with standards, and the centralized architectural scheme that relies on a unique Identity Provider (IdP) for credential issuance. To cope with these limitations, this paper describes the first implementation of the Pointcheval–Sanders Multi-Signatures (PS-MS) crypto scheme proposed by Camenisch et al. and its integration in a distributed and privacy-preserving identity management system proposed in OLYMPUS H2020 European research project. Our efficient implementation provides remarkable privacy-preservation features for identity management in online transactions leveraging p-ABC systems, including unforgeability, minimal disclosure of personal data through zero-knowledge proofs, unlinkability in online transactions and fully distributed credential issuance across different IdPs, thereby removing the IdP as a unique point of failure. The performance of the implementation has been exhaustively analyzed and evaluated with different curves, signers and number of attributes, and compared against Identity Mixer, the best known p-ABC system, outperforming significantly the credential issuance and zero-knowledge proving and verification processes (2–4 times less execution time).