Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situations, like online service provision, which involve interaction between parties who do not know (and potentially distrust) each other. The basic idea is to let parties rate each other. When a party is considered for interaction, its ratings can be aggregated in order to derive a score for deciding whether to trust it or not. While much valuable research work has been done on reputation-based trust schemes, the problem of establishing collective trust in the reputation management system itself has never been fully solved. Recently, several researchers have put forward the idea of using Distributed Ledger Technology (DLT) as the foundation for implementing trustworthy RMSs. The purpose of this paper is to identify some critical problems that arise when DLTs are used in order to manage evidence about previous interaction and compute reputations. The paper proposes some practical solutions and describes methods to deploy them on top of standard DLT of the Ethereum family.
Hao Xu, Zihan Zhou, Lei Zhang, Yunqing Sun · 5 authors
As 6G networks evolve towards a synergistic system of Communication, Sensing, and Computing, Radio Access Networks become more distributed, necessitating robust end-to-end authentication. We propose Blockchain-enabled Radio Access Networks, a novel decentralized RAN architecture enhancing security, privacy, and efficiency in authentication processes. BE-RAN leverages distributed ledger technology to establish trust, offering user-centric identity management, enabling mutual authentication, and facilitating on-demand point-to-point inter-network elements and UE-UE communication with accountable logging and billing service add-on for public network users, all without relying on centralized authorities. We envision a thoroughly decentralized RAN model and propose a privacy-preserving P2P communication approach that complements existing security measures while supporting the CSC paradigm. Results demonstrate BE-RAN significantly reduces communication and computation overheads, enhances privacy through decentralized identity management, and facilitates CSC integration, advancing towards more efficient and secure 6G networks.
With the rise of the Internet of Things (IoT) and fifth-generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third-party platform. However, since the credibility of the third-party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two-stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third-party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.
Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-based architecture. The investigated architecture combines an oracle service with a Hyperledger Fabric chaincode. We compared two logistic regression implementations in Go language—a pure chaincode and an oracle service—at various data (2–32 k instances) and network (3–13 peers) sizes. Experiments were run to assess the performance of blockchain-based model inference using 2D synthetic and EEG eye state datasets for a supervised machine learning detection task. The benchmarking results showed that the impact on performance is acceptable with the median overhead of oracle service reaching 2–4%, depending on the dimensionality of the dataset. The overhead tends to diminish at large dataset sizes with the runtime depending on the network size linearly, where additional peers increased the runtime by 6.3 and 6.6 s for 2D and EEG datasets, respectively. Demonstrated negligible difference between implementations justifies the flexible choice of model in the blockchain-based federated learning and other machine learning applications.
Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. Up to our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a {random} star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples.
Blockchain can provide a dependable environment for the Internet of Things (IoT), while the high computing power and energy required by blockchain hinder its applications in IoT. Offloading the computation at the resource-limited IoT devices to a cloud/edge computing service provider (CESP) is a feasible solution to the execution of computation-intensive blockchain tasks. The CESP provides computing resources to IoT users with a cloud and multiple edge servers that work collaboratively such that the users are able to perform mobile blockchain services. Resource allocation and pricing of computing resources at the cloud/edges have a significant impact on the revenues of CESP and users. Most of the existing works on the cooperative edge-cloud for computation offloading assumes that a user is mapped to a prespecified edge server or the cloud. However, the CESP may choose a server from either the edge servers or the cloud to run the offloaded tasks by jointly considering the cost and income of the service provisioning. In this article, we formulate a Stackelberg game with CESP as the leader and users as the followers for cloud/edge computing resource management. We prove the existence of Stackelberg equilibrium and analyze the equilibrium. We then model the resource allocation and pricing at the CESP as a mixed-integer programming problem (MIP) with the objective to optimize the CESP's revenue and propose an efficient iterative greedy-and-search-based resource allocation and pricing algorithm (IGS). The algorithm solves two subproblems comprising the CESP's revenue optimization problem: resource allocation under a given resource price and resource pricing based on a specified resource allocation scheme. The first subproblem evaluates where to execute the computing tasks via a greedy-and-search-based approach, whereas the second subproblem estimates the resource price through golden section search. We conduct experiments through simulations. Simulation results show that the proposed algorithm can effectively improve the revenue of both the CESP and the IoT terminals.
Jun Li, Yumeng Shao, Kang Wei, Ming Ding · 8 authors
Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy servers, and external attacks. To address these issues, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training on the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds$K$, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others’ trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal$K$, the learning parameters, and the proportion of lazy clients. Based on the MNIST and Fashion-MNIST datasets, we see that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than$5\%$, and the optimized$K$based on the upper bound can effectively minimize the loss function.
Nowadays the whole world is immersed in data. Starting from creating the data, using the data, sharing the data, everyone has control of their respective data. Even the companies rely highly on data as all of their storage and analysis have been computerized. In most traditional methods, the database which is being deployed is deemed trustworthy. But the threat always persists no matter how secure the database is. Attackers perform various types of attacks possible to gain access, tamper or perform any kind of compromising action on the database which will hamper the working of a company/individual.So, the goal is to achieve security against those attacks and additionally avoiding users to obtain information which they are not authorized to.However, there exists a series of relational data such as medical databases where there are two or more parties involved in a database and the trust factor automatically takes a hit. In this paper, I propose a model, that provides authorization, confidentiality, accessibility and privacy for a healthcare database. \n\n
According to a recent article published by Forbes, the use of enterprise blockchain applications by companies is expanding. Private blockchain, such as enterprise blockchain, usually uses the Raft algorithm to achieve a consensus. However, the Raft algorithm can cause network split in unstable networks. When a network applying Raft split, the TPS(Transactions Per Second) is decreased, which results in decreased performance for the entire blockchain system. To reduce the probability of network split, we select a more stable node as the next leader. To select a better leader, we propose three criteria and suggest exploiting federated learning to evaluate them for network stability. As a result, we show that blockchain consensus performance is improved by lowering the probability of network split.
Jianfeng Ma, Tao Li, Jie Cui, Zuobin Ying · 5 authors
Vehicles gather data collected by sensor nodes, combined with messages obtained from the other nodes in vehicular ad hoc networks (VANETs), to achieve safe driving. An announcement type of message is sent in the VANET; it is collected by mobile vehicles, uploaded to a cloud server for storage, and provided to other vehicles for reference. However, in an open cloud environment, plaintext data are vulnerable to unauthorized access and even malicious tampering. To solve this issue, we propose an attribute-based encryption algorithm using blockchain, which is maintained by a roadside unit (RSU). The uploader's symmetric key is recorded on the blockchain, and all uploaded and accessed transactions are recorded for auditing. Our scheme can achieve the function of securely accessing different types of announcement messages according to different vehicle attributes. Security analysis and experimental results indicate that our scheme has achieved a balance between security and efficiency.
Advanced artificial intelligence techniques, such as federated learning, has been applied to broad areas, e.g., image classification, speech recognition, smart city, and healthcare. Despite intensive research on federated learning, existing schemes are vulnerable to attacks and can hardly meet the security requirements for real-world applications. The problem of designing a secure federated learning framework to ensure the correctness of training procedure has not been sufficiently studied and remains open. In this paper, we propose VFChain, a verifiable and auditable federated learning framework based on the blockchain system. First, to provide the verifiability, a committee is selected through the blockchain to collectively aggregate models and record verifiable proofs in the blockchain. Then, to provide the auditability, a novel authenticated data structure is proposed for blockchain to improve the search efficiency of verifiable proofs and support a secure rotation of committee. Finally, to further improve the search efficiency, an optimization scheme is proposed to support multiple-model learning tasks. We implement VFChain and conduct extensive experiments by utilizing the popular deep learning models over the public real-world dataset. The evaluation results demonstrate the effectiveness of our proposed VFChain system.
Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this article, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework: 1) we design a large-scale heterogeneous WSNs collaborative identity verification protocol to ensure reliable data sources; 2) we build a hierarchical massive data aggregation scheme to collect massive IoT data efficiently and securely; and 3) we depict a blockchain-based massive IoT data management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.
Existing trust mechanisms in vehicular ad hoc networks (VANETs) suffer from a variety of vulnerabilities, such as trust inconsistency in different regions and fake trust values generated by a set of cooperating malicious nodes. In this paper, we propose a novel local trust management mechanism, ATM, to solve these problems. ATM employs active detection and blockchain techniques. Specifically, the active detection effectively filters the surrounding malicious nodes and prevents their active cooperation, while the blockchain ensures the consistency of trust data across different regions. We conduct numerical analysis to evaluate the performance of ATM. Our experiment results show that ATM performs the best among the three trust mechanisms under test. It can effectively identify malicious behaviors in terms of 95% detection accuracy and 90% deliver ratio, respectively.
Andrew R. Short, Helen C. Leligou, Efstathios Theocharis
High quality datasets have always been valuable for the creation of Machine Learning (ML) models. It therefore makes sense to provide rewards to users that participate in a Federated Learning (FL) process with such datasets. In this competitive scene, we design a solution that leverages a blockchain network, a smart contract and a model verification algorithm in order to coordinate the training process, record user performance and provide rewards in a transparent manner.
Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial intelligence while keeping raw training data on local devices. By leveraging blockchain, this article puts forward a blockchain and FL fusioned framework to manage the security and trust issues when applying FL over mobile edge networks. First, a two-layered architecture is proposed that consists of two types of blockchains: local model update chain (LMUC) assisted by device-to-device (D2D) communication and global model update chain (GMUC) supporting task sharding. The D2D-assisted LMUC is designed to chronologically and efficiently record all of the local model training results, which can help to form long-term reputations of local devices. The GMUC is proposed to provide both security and efficiency by preventing mobile edge computing nodes from malfunctioning and dividing them into logically isolated FL task-specific chains. Then a reputation-learning-based incentive mechanism is introduced to make participating local devices more trustful with a reward implemented by a smart contract. Finally, a case study is given to show that the proposed framework performs well in terms of FL learning accuracy and blockchain time delay.
Hang Chen, Syed Ali Asif, Jihong Park, Chien-Chung Shen · 5 authors
Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.
New digital technologies generate large amounts of information. This data is processed by Service Providers in order to improve and develop new services or products, but also to fund themselves. However, processing these personal data can result in the extraction of sensitive information. In consequence, it can lead to users' privacy risk. To mitigate this risk, the EU elaborated the GDPR. It forces Service Providers to have Data Subjects' explicit consent for collecting and processing their personal data. The problem is that legislative text does not define how to transparently demonstrate that they already have this consent. Also, most users do not know the rights they have over their personal data, neither this regulation provides techniques for them to be aware about what happens with it. In this paper, we propose a lightweight blockchain-based GDPR-compliant personal data management platform. It provides public access to immutable evidences that show the agreements between the Data Subjects and Service Providers. The Service Providers can demonstrate that they are fulfilling the regulation, and Data Subjects are aware about what happens with their personal data and can manage it according to their rights.
Recommendation system is widely used to predict users' interests and provide targeted products for them, which effectively facilitates users in the era of big data where information overload problem is prevalent. Unfortunately, massive data closely related to users' privacy is in high demand to produce more accurate predictions. In this case, the collection and transmission of such data is communication costly; to process and analyze such data is of high possibility to compromise users' privacy. In this paper, we propose a privacy-preserving recommendation mechanism based on blockchain, which well addresses these problems. Leveraging the inherent advantages of blockchain, we establish a completely distributed model mitigating the risk of privacy disclosure caused by central data storage. Moreover, we combine Inter-Planetary File System with blockchain to greatly improve the communication efficiency. We also introduce local sensitive hashing and local differential privacy into proposed mechanism to reduce the computation load and provide a strong privacy guarantee. The experimental results demonstrate that the proposed mechanism shows better performance on privacy preservation while maintaining desirable recommendation accuracy when compared with the baseline.
Vehicular social networks (VSNs) are the vehicular ad hoc networks (VANETs) that integrate social networks. Compared with traditional VANETs, VSNs are more suitable to serve a group of vehicles with common interests. In VSNs, vehicles can upload the necessary data in the cloud service provider (CSP) and other vehicles can query the data they are interested in through CSP, which enables VSNs to provide more user-friendly services. However, due to the wireless network communication environment, the data sent by the vehicle can easily be monitored. Adversaries are able to violate the privacy of the vehicle based on the collected data, thereby threatening the security of the entire network. In addition, if a vehicle shares malicious or false data with other vehicles, it is easy to mislead drivers and even cause serious traffic accidents. This paper proposes an effective data sharing scheme based on blockchain in VSNs. By integrating an identity based signature mechanism and pseudonym generation mechanism, we first propose an anonymous authentication mechanism as the basis for establishing trust relationships before data transmission between entities in VSNs. Then, a data sharing scheme based on blockchain is described, in which the signature mechanism and the consensus mechanism guarantee the security and traceability of data. The result of the performance analysis and the simulation experiment indicate that VAB can achieve a favourable performance compared with existing schemes.