Abstract Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.
Laasya Bangalore, Mohammad Hossein Faghihi Sereshgi, Carmit Hazay, Muthuramakrishnan Venkitasubramaniam
In this work, we introduce a lightweight secure aggregation protocol that guarantees liveness (i.e., guaranteed output delivery), robust against faulty inputs and security against malicious clients. First, we improve upon prior works in the “star”-like topology network with a central coordinating (also output) party, Bonawitz et al. (ACM CCS 2017) and Bell et al. (ACM CCS 2020), which are not robust against faulty inputs. Recent works, RoFL (Burkhalter et al.) and (concurrent work) ACORN (Bell et al.) show how to rely on zero-knowledge proofs to address such attacks at expense of significantly high computation costs. We also compare our protocol against the PRIO system by Gibbs and Boneh (USENIX 2017) which achieves the same task in an incomparable security model. We benchmark our protocol with implementation and demonstrate its concrete efficiency. Our solution scales to 1000s of clients, requires only a constant number of rounds, outperforms prior work in computational cost, and has competitive communication cost.
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most existing FL approaches rely on a centralized server for global model aggregation, leading to a single point of failure. This makes the system vulnerable to malicious attacks when dealing with dishonest clients. In this work, we address this problem by proposing a secure and reliable FL system based on blockchain and distributed ledger technology. Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors. Both theoretical and empirical analyses are presented to demonstrate the effectiveness of the proposed approach, showing that our framework is robust against malicious client-side behaviors.
<p>With the gradual development of Fourth Industrial Revolution technologies, such as artificial intelligence, the Internet of Things, and big data, and the considerable amount of data in mobile networks, low-latency communication and security management are becoming crucial. Blockchain is a data-distributed processing technology that tracks data records to support secure electronic money transactions and data security management in a peer-to-peer environment without the need of a central trusted authority. The data uploaded to the blockchain-shared ledger are immutable, making tracking integrity preservation facile. However, blockchain technology is limited because it is challenging to utilize in the industry owing to its inability to correct data, even when inaccurate data are uploaded. Accordingly, research on blockchain mechanisms that consider privacy-preserving data management is required to commercialize blockchain technology. Previously, off-chain, blacklist, and hard-fork methods have been proposed; however, their application is challenging or impractical. Therefore, to protect privacy, we propose a layered blockchain mechanism that can correct data by adding a buffer blockchain. We evaluated the latency, security, and space complexity of layered blockchains. The security and security-to-latency ratio for data management of the selective layered blockchain is 2.2 and 11.3 times higher than the conventional blockchains, respectively. The proposed selective layered blockchain is expected to promote the commercialization of blockchain technologies in various industries by protecting user privacy.</p> <p>&nbsp;</p>
Nowadays, the utilization of online auction platforms is becoming increasingly prevalent.Online auction provides a common and practical way for global buyers to compete fairly.Nevertheless, the anonymous environment may bring collusion among entities with effects on results.Compared with traditional mechanisms which rely on third-party platforms, the data auction based on smart contract can create a decentralized environment to avoid the occurrence of collusion.Meanwhile, there exists few research on the verification of its reliability and safety which is worth investigating from the perspective of formal methods.In this paper, we apply Process Algebra CSP in modeling the data auction communicating system among five key entities.In addition, we use Process Analysis Toolkit (PAT) to realize the mechanism and verify five crucial properties, including deadlock freedom, data reachability, data correctness, anti-collusion capability and data security.The verification results indicate that the architecture of data auction based on smart contract can satisfy all the above requirements.Especially, the design of asymmetric encryption for the fundamental information ensures the non-occurrence of collusion in the auction.Additionally, the digital signature generated by private key attached to the message guarantees the safety of the interaction.
Jul 1, 2023·2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)
Yang Yang, Kai Jin, Wei Liang, Yaqin Liu · 6 authors
Blockchain technology offers unique advantages in terms of decentralization, transparency, and de-anonymization. However, it also poses challenges to user anonymity and data privacy protection. Consequently, researchers have employed advanced cryptographic primitives to enhance the privacy and anonymity of blockchain-based privacy payments, as well as to extend privacy payment methods to more general forms of privacy computing. Nevertheless, relying on high-level cryptographic primitives and emerging technologies, these solutions have proven challenging for academic and industrial personnel to understand and apply. Therefore, we introduce the principle mechanisms of zero-knowledge proofs and homomorphic encryption and their typical algorithms, analyze and summarize recent research in blockchain privacy computing across several dimensions, and briefly present their potential applications.
The ability to compute on encrypted data is fast becoming a practical reality. We discuss the progress in four technologies which enable this: Trusted Execution Environments, Fully Homomorphic Encryption, Multi-Party Computation and Zero-Knowledge Proofs.
This chapter examines the implications of blockchain technology on the processing of personal data and its compliance with the general data protection regulation (GDPR) within the European Union. While the internet has revolutionized communication and database systems, it has also posed challenges to legally process personal data. However, the introduction of blockchain technology, with its cryptographic features and decentralized peer-to-peer ledger system, raises questions about the applicability of the GDPR. This chapter analyzes personal data processed in public blockchains, including the right to erasure. Additionally, the chapter explores the complexities of establishing accountability within distributed ledger technology, considering the innovative nature of blockchain and the traditional database framework upon which the GDPR was constructed. By examining the relationship between blockchain participants and fundamental data protection rights, this research aims to shed light on the intersection of blockchain technology and personal data protection.
Kai Zhang, Yan Zhang, Yanping Li, Ximeng Liu · 5 authors
Attribute-based searchable encryption (ABSE) is a promising encryption mechanism for sharing outsourced encrypted data in clouds, allowing fine-grained access control over data while searching for encrypted data. However, the access policy in the most existing ABSE schemes exists in plaintext, which could expose sensitive information about legitimate data users. Moreover, such schemes delegate complex search operations to a cloud server, which can lead to data tampering and even untrusted results, and single point of failure. In this article, we propose a blockchain (BC)-based anonymous ABSE scheme for data sharing (BADS). First, attributes of the access policy are hidden, thus, providing confidentiality to the set of attributes that satisfy the access policy. Then combining ABSE with BC have features of tamper-proof, integrity verification, and nonrepudiation. In particular, information, such as secure index is stored in BC, while encrypted data is stored in a distributed system called the interplanetary file system (IPFS) to avoid single point of failure. Finally, BADS supports the matching algorithm that perform a fixed number of pairing operations before searching algorithm. We analysis security and evaluate performance to show the efficiency and practicability of BADS.
Recent advancements in digital accident forensics, a conceptual evidence management paradigm developed using smart contracts and interplanetary file system in iov. This paper comprehensively summarizes the Smart contract implementation blockchain framework for vehicle accident investigation in IoV. We investigate comparing some review papers to find the classification of the smart contract. Using blockchain, evidence management provides an immutable and auditable method for investigating and resolving accident cases. Precisely we first investigate the security and privacy threats; therefore, Smart contracts provide effective access control for proof data and reports. On both the public and private Ethereum blockchains, the cost of setting up and executing transactions using smart contracts is assessed. However, we utilized the Inter Planetary File System most efficiently while minimizing memory and execution costs. Finally, we draw open research directions for building future digital-proof management.
The exponential growth of Big Data has heightened concerns surrounding privacy, security, and data ownership. Traditional centralized models often struggle to provide scalable and resilient privacy assurances. Distributed Ledger Technologies (DLTs) such as blockchain offer decentralized frameworks that enhance privacy, integrity, and control over Big Data assets. This article explores the potential of decentralized privacy solutions by examining the integration of DLTs into Big Data ecosystems. We discuss emerging frameworks, key technological innovations, and challenges in adoption. Graphical analysis further highlights trends in adoption and security improvements.
We will discuss Web3 Storage, Sharding and Pruning, Security Risks to Blockchain Ecosystems, Secure transaction ledgers, Blockchains in the Quantum Era, and Storing private keys.
Running a typical Bitcoin client (also called full node) needs more than 444 GB of disk space, considerable time, and computational resources to synchronize the entire blockchain, which is infeasible for resource-constrained devices. To address such concerns, the lightweight Bitcoin client proposed by Satoshi outsources most of computational and storage burdens to full nodes. Unfortunately, interacting with full nodes to query transactions leaks considerable information like addresses and transactions of lightweight client users. Thus, Bitcoin users that rely on lightweight clients are subject to de-anonymization, which defeats users privacy. Traditional schemes cannot support lightweight clients to query transactions from full nodes in an efficient yet privacy-preserving way. In this article, we propose a new efficient yet privacy-preserving transaction query scheme that specially targets the missing support for lightweight clients. We identify unique characteristics of the Bitcoin blockchain and craft a highly customized private information retrieval scheme called BIT-PIR to match the Bitcoin transaction query scenario and boost performances. Moreover, we customize a storage structure of the Bitcoin blockchain so that it further improves the query efficiency of our scheme. Finally, we develop a prototype implementation to demonstrate the feasibility of our proposed scheme.
Zheng Li, Guosheng Li, Muhammad Bilal, Dongqing Liu · 6 authors
The distribution of edge resources in the edge computing (EC) environment has an important impact on the Quality of Service (QoS) of edge services. Unreasonable server placement will inevitably lead to problems, such as server overload or underload, deteriorating workload balancing and service wait time. Therefore, the key issue to be addressed in server placement is how to enhance the QoS of edge services through efficient edge server (ES) placement strategies under multiple requirements, such as average task wait time and data privacy. EC-assisted with blockchain technology was argued to be the most potential solution. In this article, we propose a blockchain-assisted secure ES placement algorithm named ETS_GA. ETS_GA is based on the elite-preserving genetic algorithm (EGA), which is proven to converge. The premature problem of traditional genetic algorithm (GA) is effectively solved by using tabu search (TS) and niche sharing (NS). In addition, we construct an adaptive state supervising machine (ASM) to realize real-time algorithm supervision and adaptively iterate the optimization strategy. Blockchain-based privacy protection methods are also deployed in the placed servers to provide real-time privacy protection. Finally, our proposed method is experimentally compared with four baselines using the real Shanghai Telecom base station data set, whose results demonstrate the superiority of ETS_GA in terms of convergence and global search capability.
Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-preserving distributed machine learning in the Internet of Things (IoT). However, the communication overhead caused by partial model aggregations will increase the model training latency. In this article, a multilayer blockchain-enabled hierarchical FL (HFL) network is proposed for low-latency model training while ensuring data security. Meanwhile, we theoretically analyze the bottleneck of the model accuracy with the total data distance due to the imbalanced data distribution. Moreover, the mathematical expression of the model error with respect to IoT devices (IDs) association and local data distribution is provided, then the upper bound of the model error is represented by the total data distance. To further improve the learning performance, the distance-aware HFL (DAHFL) algorithm is investigated, which optimizes ID association strategy based on dual-distance, and allocates computing and communication resources alternatively. Finally, the working process of the blockchain-enabled HFL system is exhibited by the blockchain simulation platform and the efficiency of the proposed DAHFL algorithm is demonstrated by the simulation results.
Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammed Atiquzzaman
The Internet of Things (IoT) is revolutionizing numerous industrial applications by employing smart devices in manufacturing and industrial processes. Industries based on IoT generate extensive data, typically analyzed using various machine learning (ML) models. Federated learning (FL) is an emerging, privacy-preserving ML method where clients train models locally and develop a global model based on the aggregation of local models, without sharing the local data set with a third party. However, FL methods struggle to achieve trustworthiness and incorporate accountable ML principles. Blockchain technologies are being developed across different industries to enhance trust and security. This article proposes a blockchain-enabled, verifiable model for securing FL within IoT systems. Our proposed framework combines a trusted execution platform (TEE) to secure each client’s local model training process, and multisignature-powered global model verification to ensure ML model verifiability. We conducted several experiments with different data sets to assess our proposed framework. The experiments demonstrated the high efficiency and scalability of the proposed framework.
Q H Fan, Yang Xin, Bin Jia, Yang Zhang · 5 authors
Achieving efficient and secure shared data in vehicular networks is important for the development of smart transportation. Sharing data among intelligent vehicles not only enriches vehicle services but also improves traffic safety and efficiency. However, due to the specific nature of vehicular networks, security and privacy concerns prevent data providers from participating in the data sharing process. In addition, the quality of the data shared in the vehicular network is uneven and unreliable, and the reliability and authenticity of data sharing need to be further improved. In this paper, we propose a novel consortium blockchain-based trust model in vehicular networks (COBATS) to achieve secure storage and data sharing. To improve the quality of data sharing, we also design a trust management model capable of filtering malicious recommendations, which reduces the hazard of malicious nodes and ensures high-quality data sharing among vehicles. Moreover, we present a consensus mechanism with joint Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to reduce resource consumption and improve the algorithm’s efficiency. The simulation results show that COBATS can improve the security and quality of data sharing. Furthermore, our model also can effectively handle certain attacks.
Ferenc Béres, István András Seres, Domokos M. Kelen, András A. Benczúr
Network-level privacy is the Achilles heel of financial privacy in cryptocurrencies. Financial privacy amounts to achieving and maintaining blockchain- and network-level privacy. Blockchain-level privacy recently received substantial attention. Specifically, several privacy-enhancing technologies were proposed and deployed to enhance blockchain-level privacy. On the other hand, network-level privacy, i.e., privacy on the peer-to-peer layer, has seen far less attention and development. In this work, we aim to provide a peer-to-peer network simulator, ethp2psim, that allows researchers to evaluate the privacy guarantees of privacy-enhanced broadcast and message routing algorithms. Our goal is two-fold. First, we want to enable researchers to implement their proposed protocols in our modular simulator framework. Second, our simulator allows researchers to evaluate the privacy guarantees of privacy-enhanced routing algorithms. Finally, ethp2psim can help choose the right protocol parameters for efficient, robust, and private deployment.
Distributed Ledger Technologies (DLTs) and blockchain systems are used in various higher-level departments, government sectors and commercial industries. This article reviews how DLTs and blockchain systems work with IOT devices and how it provides security and scalability. The metaverse sets a new standard for social media sites and 3D virtual spaces. Moreover, the key aim of the virtual world is to safeguard the metadata of IOT customers. Additionally, crypto is an effective solution due to its openness, data integrity, and accountability characteristics. So, we review the two new burning technologies of crypto and the virtual world from a methodological perspective, i.e. data collection, saving data, allocation, integration, and data confidentiality are all aspects of data management. By each strategy, in this article, we discuss the methodological techniques of the meta chain and then investigate the potential benefits of blockchain technology. Further, we analyze how blockchain will influence other critical enabling technologies in the metaverse's service world, such as the IOT.
Federated learning (FL) has received considerable attention because it allows multiple devices to train models locally without revealing sensitive data. Well-trained local models are transmitted to a parameter server for further aggregation. The dependence on a trusted central server makes FL vulnerable to the single point of failure or attack. Blockchain is regarded as a state-of-the-art solution to decentralize the central server and provide attractive features simultaneously, such as immutability, traceability, and accountability. However, current popular blockchain systems cannot be combined with FL seamlessly. Since all local models should be collected before aggregation, the latency of FL is determined by the slowest device. The consensus process required by blockchain will increase the latency further, especially, when a large block is required for including the model. Moreover, forever-growing blockchain together with models will take up a lot of storage space, making it impractical to be deployed on lightweight devices. To address these problems, we propose a lightweight blockchain TORR for FL. A novel consensus protocol Proof of Reliability is designed to achieve fast consensus while mitigating the impact of stragglers. A storage protocol is designed based on erasure coding and periodic storage refreshing policy. With erasure coding, we take full advantage of the limited storage space of devices. With the periodic storage refreshing policy, we reduce the requirement for storage. Compared to the common blockchain-based FL system, TORR reduces the system latency, overall storage overhead, and peak storage overhead by up to 62%, 75.44%, and 51.77%, respectively.
Yijing Lin, Zhipeng Gao, Hongyang Du, Jiawen Kang · 8 authors
Blockchain-based Federated Learning (FL) technology enables vehicles to make smart decisions, improving vehicular services and enhancing the driving experience through a secure and privacy-preserving manner in Intelligent Transportation Systems (ITS). Many existing works exploit two-layer blockchain-based FL frameworks consisting of a mainchain and subchains for data interactions among intelligent vehicles, which resolve the limited throughput issue of single blockchain-based vehicular networks. However, the existing two-layer frameworks still suffer from a) strong dependency on predetermined and fixed parameters of vehicular blockchains which limit blockchain throughput and reliability; and b) high communication costs incurred by interactions among intelligent vehicles between the mainchain and subchains. To address the above challenges, we first design an adaptive blockchain-enabled FL framework for ITS based on blockchain sharding to facilitate decentralized vehicular data flows among intelligent vehicles. A streamline-based shard transmission mechanism is proposed to ensure communication efficiency almost without compromising the FL accuracy. We further formulate the proposed framework and propose an adaptive sharding mechanism using Deep Reinforcement Learning to automate the selection of parameters of vehicular shards. Numerical results clearly show that the proposed framework and mechanisms achieve adaptive, communication-efficient, credible, and scalable data interactions among intelligent vehicles.
Chengzu Dong, Jingwen Zhou, Qi An, Frank Jiang · 7 authors
In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.
Connected and autonomous vehicles, as well as micro-mobility devices (e-scooters, e-bikes, and smartphones), constantly generate tremendous amounts of mobility data, comprising spatio-temporal trajectories and other sensory data, the sharing of which can assist in different safety and intelligent transportation system applications. However, constant sharing of such data for real-time mobility decisions could violate the privacy of respective users. At the same time, malicious users could generate and share false data, which would be potentially harmful for stakeholders relying on this data for their safety and mobility decisions. In this paper, we propose a new framework: Blockchain-Enabled Location Identification and Efficient Validation with Encryption” (BELIEVE)—a blockchain-based platform with multi-party computation (MPC)—to securely validate data from a mobility source without violating the user’s privacy. The smart contractin BELIEVE first enables a source to initiate the mobility data validation process, then enables mobile peers in the vicinity to reach a consensus on the data validation using MPC without requiring the source to share raw data, thereby preserving the user’s privacy. The spatio-temporal validation is then updated on a distributed ledger (Inter Planetary File System) as an immutable transaction. Real-time data validation is challenging, considering constant mobility alongside the reduction of resource consumption at the devices and, therefore, to address this, we employ an adaptive approach to the problem where the validation frequency as well as storage on the blockchain are dynamically decided by the nodes, based on the network conditions. We evaluate BELIEVE using a simulation model developed for a portion of New York City, U.S., transportation network and it achieves lower delays and overhead.