Ahmed Samy, Ibrahim A. Elgendy, Haining Yu, Weizhe Zhang · 5 authors
Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.
Jun Huang, Debiao He, Yitao Chen, Muhammad Khurram Khan · 5 authors
The development of secure and reliable voting mechanism has attracted more and more attention from academia and industry. Many researchers are trying to design a secure and reliable voting system. However, many existing blockchain-based proposals have more or less problems, such as limited number of participants, weak fault-tolerant mechanisms and inadequate privacy protection, which makes it difficult to apply them to the real world to solve the above problems. Therefore, in this paper, we proposed a blockchain-based self-tallying voting protocol to achieve high available, secure and anonymous voting. More specifically, we adopt threshold secret sharing to deal with abstention problem (this is a difficult problem to solve in self-tallying voting systems). We adopt homomorphic encryption and zero-knowledge proof to achieve anonymity and verifiability of encrypted data. Through the analysis and performance testing, and comparison with existing similar proposals, the results show that our scheme has advantages in security and robustness, and the performance test shows that our scheme has good practicability and scalability.
Security in the communication systems rely mainly on a trusted Public Key Infrastructure (PKI) and Certificate Authorities (CAs). Besides the lack of automation, the complexity and the cost of assigning a signed certificate to a device, several allegations against CAs have been discovered, which has created trust issues in adopting this standard model for secure systems. The automation of the servers certificate assignment was achieved by the Automated Certificate Management Environment (ACME) method, but without confirming the trust of assigned certificate. This paper presents a complete tested and implemented solution to solve the trust of the Certificates provided to the servers by using the blockchain platform for certificate validation. The Blockchain network provides an immutable data store, holding the public keys of all domain names, while resolving the trust concerns by applying an automated Blockchain-based Domain Control Validation (B-DCV) for the server and client server verification. The evaluation was performed on the Ethereum Rinkeby testnet adopting the Proof of Authority (PoA) consensus algorithm which is an improved version of Proof of Stake (Po$S$) applied on Ethereum 2.0 providing superior performance compared to Ethereum 1.0.
As a novel computing paradigm, multiaccess edge computing (MEC) migrates computing and storage capabilities to edge nodes of the network to meet the requirements of executing computationally intensive or delay-sensitive tasks on intelligent vehicles. In addition, MEC fills the gap between cloud computing and terminals in vehicular networks. In the MEC system, to reduce the load on MEC servers with large-scale vehicle deployment and promote the efficient use of network resources, vehicles can also transfer tasks to neighboring resource-rich vehicles using cooperative computation offloading. However, cooperative computation offloading between vehicles faces the challenges of security and insufficient information about the server vehicle. Therefore, this paper proposes using blockchain technology to achieve efficient data sharing between vehicles and service providers (i.e., server vehicles) and ensure the security of computation offloading between vehicles. First, we design a secure data sharing architecture in blockchain-based vehicular edge computing networks. Then, a new consensus mechanism in this architecture is proposed to improve the efficiency of data sharing and prevent malicious attacks. Furthermore, we present a cooperative offloading decision-making method using an offloading game, and the Nash equilibrium of the offloading strategy is achieved using this method. The results of numerical experiments demonstrate the superior performance of the proposed method.
Murtadha Alsayegh, Peter Vanegas, Abdullah Al Redwan Newaz, Leonardo Bobadilla · 5 authors
Multi-robot task allocation is a practical way to identify synergies between robots. When all the robots within a system fall under the auspices and authority of a single organization, they can simply be compelled to share their information and participate in cooperative protocols. But when, for instance, they are rivals vying in the marketplace, their own private data may be copyrighted or sensitive, so that disclosing information may erode a competitive advantage. Yet, even limited cooperation, by offering some arbitrage of common resources (such as shared infrastructure), often reduces costs for all parties; indeed, competition and cooperation are not mutually exclusive. We examine the question of how to allocate robots to tasks optimally while ensuring that no task valuations, utilities, positions, or related data are released. We do this via an auction-based assignment algorithm implemented using secure multi-party computation operations, without requiring any trusted auctioneer. The approach offers precise and effective privacy guarantees that are stronger than present methods. We demonstrate the feasibility of the approach via tests in a case study inspired by autonomous driving. First, we tested the approach in a single-computer setup, using parties with virtual network interfaces, where we studied the effects of varying the number of parties and the associated parameters of the auction. Next, we tested the approach in a decentralized, physical test-bed using single board computers running over a WiFi LAN network. Finally, we conducted a small proof-of-concept experiment using two autonomous mobile robots performing a decentralized, private auction.
Federated learning enables multiple users to collaboratively train a global model using the users’ private data on users’ local machines. This way, users are not required to share their training data with other parties, maintaining user privacy; however, the vanilla federated learning proposal is mainly assumed to be run in a trusted environment, while the actual implementation of federated learning is expected to be performed in untrusted domains. This paper aims to use blockchain as a trusted federated learning platform to realize the missing “running on untrusted domain” requirement. First, we investigate vanilla federate learning issues such as client’s low motivation, client dropouts, model poisoning, model stealing, and unauthorized access. From those issues, we design building block solutions such as incentive mechanism, reputation system, peer-reviewed model, commitment hash, and model encryption. We then construct the full-fledged blockchain-based federated learning protocol, including client registration, training, aggregation, and reward distribution. Our evaluations show that the proposed solutions made federated learning more reliable. Moreover, the proposed system can motivate participants to be honest and perform best-effort training to obtain higher rewards while punishing malicious behaviors. Hence, running federated learning in an untrusted environment becomes possible.
Nowadays, the vast majority of Internet services used to distribute hypermedia content follow a centralized model, which is highly dependent on servers and raises several quality and security concerns. Among other issues, this centralized model creates single points of failure, requires trust on providers to avoid censorship and personal data misuse, and results in a scenario where digital content tends to disappear or be inaccessible over time, for example, when a content creator stops maintaining a site or when the content is moved to another location. To improve this, it is necessary to replicate data and follow more distributed models. Nevertheless, current platforms to distribute content in this way, either do not offer an effective mechanism to maintain the privacy of their users or they offer full-anonymity, which contributes to the dissemination of content that goes beyond the law and moral standards of many users. This paper proposes a novel distributed architecture that enables hypermedia resource distribution ensuring censorship resistance and conditional k-anonymity. In the proposed system, users form groups to share hypermedia content where the anonymity of the publisher is preserved only if the publication follows a set of rules defined by the group. To this end, the proposed system uses threshold discernible ring signatures to enable conditional k-anonymity, the Ethereum blockchain platform to manage groups and user identities, and the InterPlanetary File System to store and share hypermedia resources in a distributed way. This document provides the design for the proposed architecture and protocols, it evaluates system risks and its security properties, and it discusses the proposal in general terms.
Jianbin Li, Shike Li, Long Cheng, Qingzhi Liu · 6 authors
Consensus-based Speed Advisory System (CSAS) is used to recommend a consensus speed to a group of vehicles for specific application purposes, such as minimizing emissions or energy consumption. To remedy data privacy concerns for speed advisory services, the latest works have investigated how to get an optimal speed in a privacy-preserving manner. However, almost all the designs are based on a centralized architecture, which could still meet service trust issues, such as that a random speed could be recommended when the central server meets cyber incursion attacks. To address the problem, in this paper we propose BSAS, a trustworthy and privacy-preserving CSAS over the blockchain. Specifically, BSAS follows a fully decentralized architecture with cryptographic primitives to guarantee service trust and data privacy. Moreover, to encourage vehicles to participate in the service computing process, a value-driven incentive mechanism is also employed. We present the detailed design and implementation of BSAS, and our emulation results show that the proposed BSAS can achieve promising system performance in terms of real-time speed recommendation in a trustworthy and privacy-preserving way.
Jafar A. Alzubi, Omar A. Alzubi, Ashish Singh, R. Manikandan
Industrial cloud computing and Internet of Things have transformed the healthcare industry with the rapid growth of distributed healthcare data. Security and privacy of healthcare data are crucial challenges in the healthcare industry. This article proposes a novel technique using deep learning and blockchain techniques for electronic health record privacy-preservation. The processed dataset classified normal and abnormal users using the convolutional neural network approach. Then, by using blockchain integrated with a cryptography-based federated learning module, the abnormal users have been processed and removed from the database along with the accessibility for the health records. The simulation has been done in the Python tool and experimental results show that the model’s classification results and performance are better than other existing techniques.
The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated is the performance superiority of our proposal as compared with the baselines.
The security of the tax system is directly related to the development of a country. The conventional process of tax payment laborious steps, so this process becomes a cause of irregularities among taxpayers and tax authorities, increasing the rate of corruption in tax collection. Blockchain, as a distributed ledger technology, its unique advantages and promising applications in taxation offer an effective solution to the problems of electronic taxation. However, the transparency of blockchain exists the risk of privacy disclosure, the high degree of anonymity brings the problem of lack of user supervision. Therefore, for balancing the contradiction of taxpayer privacy and supervision, we propose a blockchain-based self-certified and anonymous e-taxing scheme, which uses blockchain as the underlying support, and utilizes cryptography technology such as self-certified public key, Diffie-Hellman, to reduce the taxpayer's reliance on the certificate authority, and protects the taxpayer's anonymity while realizing the tracking of the real identity of malicious taxpayers. The security analysis proves that the scheme has the properties such as anonymity, conditional privacy and unforgeability, etc. Finally, performance analysis shows that compared with similar schemes, the scheme significantly improves the registration efficiency, proving its practicability and implementability.
Seth Larweh Kodjiku, Yili Fang, Tao Han, Kwame Omono Asamoah · 9 authors
Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.
The rapid increase in the number of mobile smart devices has led to an exponential growth in user data. With the access of a large number of heterogeneous edge devices and the in-depth development of edge computing technology in various fields, it has brought huge challenges to the security management and data privacy protection in the edge computing architecture. Therefore, this paper proposes a lightweight edge computing data privacy-preserving scheme based on blockchain and homomorphic encryption. Above all, set edge computing servers as distributed nodes of the blockchain network and utilize the decentralization of blockchain and the non-tamperable characteristics of information to ensure the distributed storage of data and information recording. Then, designing a blockchain data encryption transmission scheme based on homomorphic encryption which could guarantee data transmission between edge nodes in the form of homomorphic ciphertext. Meanwhile, supporting the verification of the legality and correctness of the transaction in the form of ciphertext. Simulation experiments show that the method proposed in this paper can carry out effective data encryption transmission and has certain application potential.
As the voucher for identity, digital certificates and the public key infrastructure (PKI) system have always played a vital role to provide the authentication services. In recent years, with the increase in attacks on traditional centralized PKIs and the extensive deployment of blockchains, researchers have tried to establish blockchain-based secure decentralized PKIs and have made significant progress. Although blockchain enhances security, it brings new problems in scalability due to the inherent limitations of blockchain’s data structure and consensus mechanism, which become much severe for the massive access in the era of 5G and B5G. In this paper, we propose ScalaCert to mitigate the scalability problems of blockchain-based PKIs by utilizing redactable blockchain for "on-cert" revocation. Specifically, we utilize the redactable blockchain to record revocation information directly on the original certificate ("on-cert") and remove additional data structures such as CRL, significantly reducing storage overhead. Moreover, the combination of redactable and consortium blockchains brings a new kind of attack called deception of versions (DoV) attack. To defend against it, we design a random-block-node-check (RBNC) based freshness check mechanism. Security and performance analyses show that ScalaCert has sufficient security and effectively solves the scalability problem of the blockchain-based PKI system.
An accumulator is a function that hashes a set of inputs into a short, constant-size string while preserving the ability to efficiently prove the inclusion of a specific input element in the hashed set. A concrete accumulator is constructed by using strong RSA assumption. Thanks to their practical features, accumulators are used in various protocols such as zero-knowledge proofs, group signatures, and blockchain. However, lattice-based accumulators are not as well studied as the strong RSA assumption. In 2019, Ling et al. constructed a lattice-based accumulator that is enable to update the member in the list, called LLNW in this paper. However, the update algorithm of the LLNW scheme is not complete, since it requires recalculation to any member regardless of whether or not the member is updated. In this paper, we propose an efficient update algorithm, called EfficientAccWitUpdate, to LLNW, which enables us to update members more efficiently than that in LLNW. In our method, only a member who updates requires recalculation. Specifically, the number of multiplications required for updating in EfficientAccWitUpdate is$\frac{1}{2}$of LNWX, and the number of additions required for updating in EfficientAccWitUpdate is$\frac{nk+1}{2nk-1}$of LNWX. Consequently, by incorporating the EfficientAccWitUpdate accumulator scheme into the zero-knowledge protocol, group signature, and blockchain, it is possible to realize a more efficient application.
User privacy is an attractive and valuable task to the success of blockchain systems. However, user privacy protection's performance and data capacity have not been well studied in existing access control models of blockchain systems because of traceability and openness of the P2P network. This paper focuses on investigating performance and data capacity from a blockchain infrastructure perspective, which adds secondary encryption to shield confidential information in a non-invasive way. First, we propose an efficient asymmetric encryption scheme by combining homomorphic encryption and state-of-the-art multi-signature key aggregation to preserve privacy. Second, we use smart contracts and CA infrastructure to achieve attribute-based access control. Then, we use the non-interactive zero-knowledge proof scheme to achieve secondary confidentiality explicitly. Finally, experiments show our scheme succeeds better performance in data capacity and system than other schemes. This scheme improves availability and robust scalability, solves the problem of multi-signature key distribution and the unlinkability of transactions. Our scheme has established a sound security cross-chain system and privacy confidentiality mechanism and that has more excellent performance and higher system computing ability than other schemes.
Contact tracing is a critical tool for restricting the transmission of any infectious disease. This basic mechanism aids in identifying and breaking the chain of transmission by separating a healthy person from an infected person. Different nations and health institutions use various contact tracing methods to identify the transmission network and control the outbreak. However, the users face severe security and privacy risks, including storing the history of their locations, personal and device information. Subsequently, this personalized information could be utilized by a cyber-attacker for malicious purposes. In this study, the privacy and security risks of contact tracing technologies are identified, and a potential secure solution is proposed based on the blockchain network. The developed model is a decentralized application (or D-App) implemented using Ethereum smart contracts. The Bluetooth technology is used to identify contacts, and their data gets stored in the blockchain. This decentralization approach of D-App makes it an ideal framework for developing contact tracing applications.
With the advent of the Big Data era, industry, business and academia have developed various data exchange schemes to make data more economically beneficial. Unfortunately, most of the existing systems provide only one-time data exchanges without the ability to track the provenance and transformations of datasets. In addition, existing systems encrypt the data to protect data privacy, which hinders demanders from verifying the correctness of the data and evaluating its value.To provide data traceability and privacy while ensuring fairness during data exchanges, we design and implement ZKDET, a traceable data exchange scheme based on non-fungible token and zero-knowledge, which is able to (i) track all transformations of data during their lifecycle and record them on the blockchain; (ii) provide zero-knowledge proofs to securely guarantee that all complex transformations and data contents are correct and meet specific requirements; and (iii) warrant exchange fairness and data privacy in public storage platforms. Security analysis and evaluations on ZKDET show that it can support traceable data exchange while preserving data privacy and maintaining high throughput despite large data volumes.
With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5 G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.
Abstract With the recent advancements in the Internet of Things, cloud computing has emerged as an important industrial technology that assists in various data analysis operations. However, the remote locality of cloud servers and scalability issues of cloud computing make it unsuitable for real‐time computing‐intensive applications. Fog computing strives to support cloud computing in meeting scalability demands by providing location‐sensitive services closer to end devices. With decentralized heterogeneous resource capabilities, fog architecture can handle several computation‐intensive and delay‐sensitive user requests. Although deploying service providers in an untrustworthy environment makes it challenging to assess the trustworthy acquired services. Conspicuously, in this article, we present a trusted task offloading and resource allocation using blockchain technology. To start with, we analyze direct and indirect trust with a subjective logical aggregation approach using a distributed trust assessment approach. Additionally, we examined the various quality of service parameters and constructed a smart contract that utilizes the state‐of‐the‐art deep reinforcement learning algorithm, namely Deep Deterministic Policy Gradient, to maximize fog revenue while serving as many user requests as possible. The entire process from task generation to results calculation is assisted by blockchain and offloading task transactions are stored in the secure, immutable, and tamper‐resistant ledger. To assess the effectiveness of our proposed scheme, we compared the simulation results with other baseline schemes over different performance metrics in terms of reward, service latency, energy consumption, task drop ratio, and transaction success rate. The results suggest that enabling trust computation improves transaction success by 21%.