In Vehicular Ad-hoc Networks (VANETs), privacy protection and data security during network transmission and data analysis have attracted attention. In this paper, we apply deep learning, blockchain, and fully homomorphic encryption (FHE) technologies in VANETs and propose a Decentralized Privacy-preserving Deep Learning (DPDL) model. We propose a Decentralized VANETs (DVANETs) architecture, where computing tasks are decomposed from centralized cloud services to edge computing (EC) nodes, thereby effectively reducing network communication overhead and congestion delay. We use blockchain to establish a secure and trusted data communication mechanism among vehicles, roadside units, and EC nodes. In addition, we propose a DPDL model to provide privacy-preserving data analysis for DVANET, where the FHE algorithm is used to encrypt the transportation data on each EC node and input it into the local DPDL models, thereby effectively protecting the privacy and credibility of vehicles. Moreover, we further use blockchain to provide a decentralized and trusted DPDL model update mechanism, where the parameters of each local DPDL model are stored in the blockchain for sharing with other distributed models. In this way, all distributed models can update their models in a credible and asynchronous manner, avoiding possible threats and attacks. Extensive simulations are conducted to evaluate the effectiveness, practicality, and robustness of the proposed DVANET system and DPDL models.
In recent years, traditional logistics systems are developing toward intelligence based on the Internet of Things (IoT). Sensing devices throughout the logistics network provide strong support for smart logistics. However, due to the insufficient local computing and storage resources of IoT devices, logistics records with sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this study, we propose a blockchain-assisted secure storage scheme for logistics data. To be specific, this scheme can be briefly divided into two parts. The first part involves data generation and aggregation, session establishing, records encryption and storage, wherein a blockchain network is used to assist the cloud server with data storage, and smart contracts are deployed to provide reliable storage interfaces. In the second part, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process. Also, the stored records can be securely audited by leveraging the deployed blockchain network. Finally, we analyze the security and privacy properties of this scheme and evaluate its performance in terms of computation and communication overhead by developing an experimental platform. The experimental results indicate that the performance of our scheme is acceptable.
Reputation/trust-based blockchain systems have attracted considerable research interests for better integrating Internet of Things with blockchain in terms of throughput, scalability, energy efficiency, and incentive aspects. However, most existing works only consider static adversaries. Hence, they are vulnerable to slowly adaptive attackers, who can target validators with high reputation value to severely degrade the system performance. Therefore, we introduce$\textsf{zkRep}$, a privacy-preserving scheme tailored for reputation-based blockchains. Our basic idea is to hide both the identity and reputation of the validators by periodically changing the identity and reputation commitments (i.e., aliases), which makes it much more difficult for slowly adaptive attackers to identify validators with high reputation value. To realize this idea, we utilize privacy-preserving Pedersen-commitment-based reputation updating and leader election schemes that operate on concealed reputations within an epoch. We also introduce a privacy-preserving identity update protocol that changes the identity and time-window-based cumulative reputation commitments during each epoch transition. We have implemented and evaluated$\textsf{zkRep}$on the Amazon Web Service. The experimental results and analysis show that$\textsf{zkRep}$achieves great privacy-preserving features against slowly adaptive attacks with little overhead.
As the future energy infrastructure, smart grid aims to overcome the disadvantages of traditional power grid, e.g., low efficiency and unstable service. However, the frequent collection and analysis of the userâs electricity data may bring various security and privacy threats. Besides, the traditional centralized data storage model in the smart grid is prone to the single point of failure. To address these challenges, in this article, for fair and secure smart grid communication, a blockchain-based novel paradigm, named BBNP, is proposed. Specifically, based on the pseudorandom function and auxiliary information generation and sharing technology, a lightweight data aggregation protocol is designed first to protect the userâs data privacy and ensure communication confidentiality. Then, a novel efficient authentication mechanism is proposed to generate and share session keys in a noninteractive way, which is leveraged for MAC authentication to achieve data integrity of the transmitted data. After that, based on the subjective logic reputation model, a blockchain node consensus mechanism is studied to efficiently store smart grid big data and effectively solve the single point failure problem. By constructing the long-term reputation model for consensus nodes (CNs) and integrating batch verification technology, the problems of CN fair selection and scalability of large-scale nodes are solved simultaneously. Finally, the performance evaluation indicates that BBNP outperforms the state-of-the-art similar schemes in computing complexity, communication cost, system availability, and fairness of block generation.
Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain usersâ sensitive information. When guaranteeing the utility of data, we need to consider privacy of usersâ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification (PPDC) scheme for IoT. We put forth a new controllably linkable group signature (CL-GS) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance ofPPDC.
The majority of hacking accidents in cryptocurrency occur when the information of a cryptocurrency wallet is stolen. Since the cryptocurrency wallet is simply used for a key storage, when connecting to a transaction network, it is vulnerable for a key theft. Blockchain is not traceable, but it should communicate to applicate data of blockchain. To communicate to applicate data of blockchain, this study proposes a key protocol design to secure cryptocurrency transactions for user privacy of cryptocurrency to resolve the drawback of decentralized exchange. The key protocol includes a session key for a blockchain data structure and the Federated Byzantine Agreement (FBA) for the key-exchange agreement among users. In F-measure model, the values of Key Cluster Mode, Test Session key Mode and Original Session key Mode resulted in True Positive Ratio greater than 0.5 and False Positive Ratio lesser than 0.5. Therefore, the key protocol model has optimal security. In addition, computation costs of the protocol improve by compared with former studies. It may be played an important role in the cryptocurrency hacking accident and supported robust cryptocurrency market The study guarantees the security of cryptocurrency users without decentralized exchange, and it is scalable to other areas by using secure distributed networks.
Youliang Tian, Ta Li, Jinbo Xiong, Md Zakirul Alam Bhuiyan · 6 authors
Edge services provide an effective and superior means of real-time transmissions and rapid processing of information in the Industrial Internet of Things (IIoT). However, the continuous increase of the number of smart devices results in privacy leakage and insufficient model accuracy of edge services. To tackle these challenges, in this article, we propose a blockchain-based machine learning framework for edge services (BML-ES) in IIoT. Specifically, we construct novel smart contracts to encourage multiparty participation of edge services to improve the efficiency of data processing. Moreover, we propose an aggregation strategy to verify and aggregate model parameters to ensure the accuracy of decision tree models. Finally, based on the SM2 public key cryptosystem, we protect data security and prevent data privacy leakage in edge services. Theoretical analysis and simulation experiments indicate that the BML-ES framework is secure, effective, and efficient, and is better suitable to improve the accuracy of edge services in IIoT.
Ponlawat Weerapanpisit, Sergio Trilles, JoaquıÌn Huerta, Marco PaĂŹnho
Social Internet of Things (SIoT) is a concept that integrates the Internet of Things and human social networks. An SIoT system has to store and manage device reputation values, which are used by end devices to determine the trustworthiness of another one. This device trustworthiness can also be affected by its geographical location. In this work, we introduced an architecture that includes the geospatial context in the part concerned with reputation management. The proposed architecture is based on the cloud-fog-edge architecture and uses the fog layer as the management system. The devices in the fog layer form an Ethereum Blockchain network and store the Smart Contracts. These in turn allow the management functionalities to be carried out in a decentralised, transparent and secure way, which are the advantages of Blockchain. To enable the characteristics with a geospatial component, it is necessary to apply a geocoding technique. This work shows how geocoding techniques can be adapted to cover the main geospatial functionalities and compares two geocoding options (Geohash or S2). The results showed that it is possible to include the geospatial context in a decentralised reputation management system by using hierarchical geocoding techniques, and the experiments showed that both Geohash and S2 can offer a similar performance in the proposed architecture.
After the General Data Protection Regulation (GDPR) was introduced, some organizations and big data companies shared data without conducting any privacy protection and compliance authentication, which endangered user data security, and were punished financially for this reason. This study proposes a blockchain-based GDPR compliance data sharing scheme, aiming to promote compliance with regulations and provide a tool for interaction between users and service providers to achieve data security sharing. The zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK) algorithm is adopted for protecting data and ensure that the userâs private data can satisfy the individual requirements of the service provider without exposing user data. The proposed scheme ensures mutual authentication through the Proof of Authority consensus based on the Committee Endorsement Mechanism (CEM-PoA), and prevents nodes from doing evil using the reputation incentive mechanism. Theoretical analysis and performance comparison indicate that the scheme meets the confidentiality, availability, and other indicators. It has superiority in efficiency and privacy protection compared with other schemes.
With recent developments in communication technologies, vehicular networks have become a reality with various applications. However, the cybersecurity aspect of vehicular networks is still an open issue that needs to be addressed with novel defence mechanisms against attacks. This paper first presents the state-of-the-art communication technologies in vehicular networks (either inter-vehicle networking or in-vehicle networking) along with their applications. Then we explore novel technologies including machine learning and blockchain as cybersecurity defence mechanisms in vehicular networks. Based on the extensive survey, we highlight some insights for future research to secure vehicular networks.
Abstract The explosive growth of big data is pushing forward the paradigm of cloud-based data store today. Among other, distributed storage systems are widely adopted due to their superior performance and continuous availability. However, due to the potentially wide attacking surfaces of the public cloud, outsourcing data store inevitably raises new concerns on user privacy exposure and unauthorized data access. Besides, directly introducing a centralized third-party authority for query authorization management does not work because it still can be compromised. In this paper, we propose a blockchain-assisted framework that can support trustworthy data sharing services. In particular, data owners allow to outsource their sensitive data to distributed systems in encrypted form. By leveraging smart contracts of blockchain, a data owner can distribute secret keys for authorized users without extra round interaction to generate the permitted search tokens. Meanwhile, such blockchain-assisted framework naturally solves the trust issues of query authorization. Besides, we devise a secure local index framework to support encrypted keyword search with forward privacy and mitigate blockchain overhead. To validate our design, we implement the prototype and deploy it at Amazon Cloud. Extensive experiments demonstrate the security, efficiency, and effectiveness of the blockchain-assisted design.
Abstract Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even if they have provided valuable solutions. Second, unfair evaluation and reward distribution can lead to low enthusiasm for work. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as âfalse-reportingâ and a Shapley value -based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.
ChunâWei Tsai, YiâPing Phoebe Chen, TzuâChieh Tang, Yuchen Luo
The unlimited possibilities of machine learning have been shown in several successful reports and applications. However, how to make sure that the searched results of a machine learning system are not tampered by anyone and how to prevent the other users in the same network environment from easily getting our private data are two critical research issues when we immerse into powerful machine learning-based systems or applications. This situation is just like other modern information systems that confront security and privacy issues. The development of blockchain provides us an alternative way to address these two issues. That is why some recent studies have attempted to develop machine learning systems with blockchain technologies or to apply machine learning methods to blockchain systems. To show what the combination of blockchain and machine learning is capable of doing, in this paper, we proposed a parallel framework to find out suitable hyperparameters of deep learning in a blockchain environment by using a metaheuristic algorithm. The proposed framework also takes into account the issue of communication cost, by limiting the number of information exchanges between miners and blockchain.
Yilong Hui, Yuanhao Huang, Zhou Su, Tom H. Luan · 7 authors
The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
Moayad Aloqaily, Ismaeel Al Ridhawi, Mohsen Guizani
The aerial capabilities and flexibility in movement of Unmanned Aerial Vehicles (UAVs) has enabled them to adaptively provide both traditional and more contemporary services. In this article, we introduce a solution that integrates the capabilities of both UAVs and Unmanned Ground Vehicles (UGVs) to provide both intelligent connectivity and services to both aerial and ground connected devices. A cooperative solution is adopted that considers nodesâ power and movement constraints. The UAV and UGV cooperative process ensures continuous power availability to UAVs to support seamless and continuous service availability to end-devices. A Federated Learning (FL) approach is adopted at the edge to ensure accurate and up-to-date service provisioning in accordance with the surrounding environment and network constraints. Moreover, Blockchain technology is used to decentralize the provisioning and control aspects, and ensure authenticity and integrity. Extensive simulations are conducted to test the soundness and applicability of the proposed solution. Results show significant improvement in terms of connectivity, service availability, and UAV energy enhancements when compared to traditional mobile and vehicular communication techniques.
Anwar Ali Sathio, Mazhar Ali Dootio, Abdullah Lakhan, Mujeeb Ur Rehman · 6 authors
These days, the usage of healthcare applications in the secure and authenticate blockchain network has been growing progressively. The distributed healthcare applications can store and shared data with another node without any centralized authority by exploiting the blockchain technology. However, existing proof of work inside blockchain did not consider the anomaly detection in healthcare networks and mobility of the workload widely ignored in the literature studies. In this paper, the study investigates the travelling salesman problem with the scheduling, threshold, and anomaly detection constraints in distributed fog nodes. The fog nodes are local servers and implementing inside the hospital to facilitate the users from different healthcare services. The study devises the mobility scheduler anomaly detection (MSAD) schemes which consist of two phases, e.g., the initial assignment of healthcare workloads to optimal fog nodes and anomaly detection and validation in the healthcare blockchain network. Simulation results show that MSAD outperformed in terms of scheduling, threshold, anomaly detection in the healthcare blockchain network as compared to baseline studies.
Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang · 7 authors
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalisation and accuracy.
This work presents a complex privacy-preserving solution based on attribute-based credentials and smart contract techniques for emerging parking services in city zones. Our system provides the full set of privacy-enhancing features such as anonymity, untraceability, and unlinkability of user parking registrations. Thanks to that it prevents the city and service providers from profiling and tracking the users (e.g., their movement). Furthermore, we involved smart contracts and the underlying decentralized Blockchain technology in payment and verification phases to prevent the presence of a single point of failure in those processes which can endanger the systemâs security and availability. We provide the full cryptographic specification of the system, its security analysis, and the implementation results in this paper.
Delegation of cryptographic signing rights has found many application in the literature and the real world. However, despite very advanced functionalities and specific use cases, existing solutions share the natural limitation that the number of usages of these signing rights cannot be efficiently limited, but users can at most be disincentivized to abuse their rights. In this paper, we suggest a solution to this problem based on blockchains. We let a user define a smart contract defining delegated signing rights, which needs to be triggered to successfully sign a message. By leveraging the immutability of the blockchain, our construction can now guarantee that a user-defined threshold of signature invocations cannot be exceeded, thereby circumventing the need for dedicated hardware or similar assistance in existing constructions for one-time programs. We discuss different constructions supporting different features, and provide concrete implementations in the Solidity language of the Ethereum blockchain, proving the real-world efficiency and feasibility of our construction.
Today's computing is characterized by an increasing degree of complexity, comprehensiveness and collaboration. The complexity can be observed by the wide application of gigantic models with a huge number of parameters and structures of an unprecedented level of sophistication. The comprehensiveness is best illustrated by the high heterogeneity of data both in terms of format and source. The collaboration, finally, becomes an obvious trend when computing systems grow more open and decentralized in which various entities interact to achieve collective intelligence with the presence of potentially malicious behavior. Trust, therefore, has become critical at multiple levels: At model level to assure its integrity, fairness and interpretability; At data level to safeguard data quality, compliance and privacy; At system level to govern resilience, performance and incentive. Moreover, the notion of trust has long been discussed in different domains in both academia and industry with different definition and understanding. The Third International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD'21) will be held as a joint workshop with the special-themed "Trust Day" of KDD 2021, which has therefore aimed to bring together researchers, practitioners and experts from various communities to exchange and explore ideas, frontiers, opportunities and challenges under the broad theme of "trust" in a highly interdisciplinary manner.
With the continuous expansion of Internet of Things (IoT) devices, edge computing mode has emerged in recent years to overcome the shortcomings of traditional cloud computing mode, such as high delay, network congestion, and large resource consumption. Thus, edge-thing systems will replace the classic cloud-thing/cloud-edge-thing systems and become mainstream gradually, where IoT devices can offload their tasks to neighboring edge nodes. A common problem is how to utilize edge computing resources. For the sake of fairness, double auction can be used in the edge-thing system to achieve an effective resource allocation and pricing mechanism. Due to the lack of third-party management agencies and mutual distrust between nodes, in our edge-thing systems, we introduce blockchains to prevent malicious nodes from tampering with transaction records and smart contracts to act as an auctioneer to realize resources auction. Since the auction results stored in this blockchain-based system are transparent, they are threatened with inference attacks. Thus in this paper, we design a differentially private combinatorial double auction mechanism by exploring the exponential mechanism such that maximizing the revenue of edge computing platform, in which each IoT device requests a resource bundle and edge nodes compete with each other to provide resources. It can not only guarantee approximate truthfulness and high revenue, but also ensure privacy security. Through necessary theoretical analysis and numerical simulations, the effectiveness of our proposed mechanisms can be validated.
Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection were largely dependent on a trusted centralized server, which might suffer from single point of failure, the lack of transparency and so on. Some works recently proposed blockchain-based crowdsensing, which utilized reputation values stored on blockchains to select trusted workers. However, the transparency of blockchains enables attackers to effectively infer private information about workers by the disclosure of their reputation values. In this article, we proposed the TrustWorker, a trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing. By taking the advantages of blockchains such as decentralization, transparency and immutability, our TrustWorker could make the worker selection process trustworthy. To protect workersâ reputation privacy in our TrustWorker, we adopted a deterministic encryption algorithm to encrypt reputation values and then selected the top$N$workers in the light of secret minimum heapsort scheme. Finally, we theoretically analyzed the effectiveness and efficiency of our TrustWorker, and then conducted a series of experiments. The theoretical analysis and experiment results demonstrate that our TrustWorker can achieve trustworthy worker selection, while ensuring the workersâ privacy and the high quality of sensing services.
Haiqin Wu, Boris DĂŒdder, Liangmin Wang, Shipu Sun · 5 authors
The ubiquity of crowdsourcing has reshaped the static sensor-enabled data sensing paradigm with cost efficiency and flexibility. Still, most existing triangular crowdsourcing systems only work under the centralized trust assumption and suffer from various attacks mounted by malicious users. Although incorporating the emerging blockchain technology into crowdsourcing provides a possibility to mitigate some of the issues, how to concretely implement the crucial components and their functionalities in a verifiable and privacy-aware manner remains unaddressed. In this article, we present BRPC, a blockchain-based decentralized system for general crowdsourcing. BRPC integrates the confident-aware truth discovery algorithm to provide task requesters with reliable task truths while evaluating each workerâs data quality. To mitigate the biased evaluation of malicious requesters, we propose a privacy-aware verification protocol leveraging the threshold Paillier cryptosystem, with which a certain number of workers can collaboratively verify the evaluation results without knowing any sensory data. Furthermore, we define the three roles of a user and elaborate a comprehensive reputation evaluation model enforced by smart contracts for its trustworthy running. Financial and social incentives are both offered to motivate usersâ honest participation. Finally, we implement a prototype of BRPC and deploy it on the Ethereum blockchain. Theoretical analyses and experiment results show its security and practicality.
The Internet of things (IoT) is an active, real-world area in need of more investigation. One of the top weaknesses in security challenges that IoTs face, the centralized access control server, which can be a single point of failure. In this paper, Dynamic-IoTrust, a decentralized access control smart contract based aims to overcome distrusted, dynamic, trust and authentication issues for access control in IoT. It also integrates dynamic trust value to evaluate users based on behavior. In particular, the Dynamic-IoTrust contains multiple Main Smart Contract, one Register Contract, and one Judging Contract to achieve efficient distributed access control management. Dynamic-IoTrust provides both static access rights by allowing predefined access control policies and also provides dynamic access rights by checking the trust value and the behavior of the user. The system also provides to detected user misbehavior and make a decision for user trust value and penalty. There are several levels of trusted users to access the IoTs device. Finally, the case study demonstrates the feasibility of the Dynamic-IoTrust model to offer a dynamic decentralized access control system with trust value attribute to evaluate the internal user used IoTs devices.