Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients’ health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.
Data are generated by Internet of Things (IoT) devices and centralized at a cloud server, that can later be traded with third parties, i.e., data marketing, to enable various data-intensive applications. However, the centralized approach is recently under debate due to the lack of (1) transparent and distributed marketplace management, and (2) marketing fairness for both IoT users (data sellers) and third parties (data buyers). In this paper, we propose a Blockchain-Cloud Transparent Data Marketing (Block-DM) with consortium management and executable fairness. First, we introduce a hybrid data-marketing architecture, where the cloud acts as an efficient data management unit and a consortium blockchain serves as a transparent marketing controller. Under the architecture, consent-based secure data trading and identity privacy for data owners are achieved with the distributed credential issuance and threshold credential openings. Second, with a consortium committee, we design a fair on/off-chain data marketing protocol. By financial incentives and succinct ‘commitments’ of marketing operations, the protocol can achieve the marketing fairness and effective detection of unfair marketing operations. We demonstrate the security of Block-DM with thorough analysis. We conduct extensive experiments with a consortium blockchain network on Hyperledger Fabric to show the feasibility and practicality of Block-DM.
With the popularity of cloud computing services, an increasing number of users begin to use subscription-based services. Due to the semi-trusted cloud servers that may access the outsourced data, and malicious senders who may publish unauthorized data or junk data, access control encryption (ACE) schemes have been studied recently to enforce secure data write control as well as read control. However, their access control policies are specified by the authority or publishers, which do not apply to the subscriptions. In this paper, we propose DSFlow, a secure and fine-grained flow control system for subscription-based data services. DSFlow is designed in the cloud-edge computing architecture, which employs edge nodes to control the communications between publishers and cloud servers by sanitizing the original ciphertexts to resist malicious publishers, and allows any valid subscriber to decrypt the sanitized ciphertexts in cloud. We introduce a receiver-policy attribute-based ACE (RA-ACE) scheme for DSFlow, which embeds the fine-grained access control policy within the receiver's decryption key. We give a concrete construction of RA-ACE from key-policy attribute-based encryption, structure-preserving signature and non-interactive zero-knowledge proof, and formally prove the no-read rule and no-write rule of RA-ACE. The experiments demonstrate the efficiency of DSFlow compared with existing schemes.
Abdullah Yousafzai, Latif U. Khan, Umer Majeed, Owais Hakeem · 5 authors
Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks. Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers. In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e. mobile devices) can choose one FL task publisher over another for participation in the training of a global model. We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers. Experimental results indicate the efficacy of the proposed framework.
Abstract The KRAKEN project aims to enable the sharing, brokerage, and trading of personal data including sensitive data (e.g., educational and health records and wellbeing data from wearable devices) by returning its control to both data subjects/data providers throughout the entire data lifecycle. The project is providing a data marketplace which will allow the sharing of personal data and its usage for research and business purposes, by using privacy-preserving cryptographic tools. KRAKEN is developing an advanced platform to share certified information between users and organizations by leveraging on distributed ledger technology, promoting the vision of self-sovereign identity solutions (ensuring users’ consent and data control in a privacy-friendly way), preserving security, privacy, and the protection of personal data in compliance with EU regulations (e.g., GDPR). The feasibility of the KRAKEN solution will be tested through two high-impact pilots in the education and healthcare fields.
Secure cloud search service allows resource-constrained clients to effectively search over encrypted cloud storage. Towards enabling owner-enforced search authorization, the notion of attribute-based keyword search (ABKS) has been introduced and widely deployed in practice. To enhance traditional security of ABKS, two state-of-the-art solutions are presented to address keyword guessing attacks or setup inconsistency for secret key. Nevertheless, they have not simultaneously considered the following threats to a data user: (i) inconsistent secret key/cipher-index caused by outside dishonest authority and/or data owner; (ii) algorithm substitution attacks (ASA) launched by inside adversarial eavesdropping. These attacks may unfortunately lead to cloud data breach and user information exposure. To tackle such outside and inside threats, we introduce subversion-resistance and consistency for secure and fine-grained cloud document search services. In particular, we propose a consistent ABKS system with cryptographic reverse firewalls (CRF). Technically, we refer to verifiable functional encryption and employ non-interactive zero-knowledge proofs of discrete logarithm equality to ensure strong input consistency for ABKS. In addition, we build a trusted CRF zone for sanitizing algorithm outputs against ASA attacks. Moreover, we formalize the security model and formally prove security of our system. To clarify practical performance, we implement state-of-the-art solutions and our system in real cloud environment based on Enron dataset. The results show that our system achieves more enhanced security properties without obviously sacrificing performance. In particular, our system achieves comparable time and storage cost for document-index encryption and document search, as compared to state-of-the-art solutions.
This paper describes a blockchain-based privacy preservation scheme for medical health records. IoT, e-health, and cloud-based hospital management systems have all become important components of our healthcare system. Hospital patient data is extremely sensitive and preserving patient data privacy appears to be difficult. The current method for data storage is to use a cloud storage system. All of this is done through a centralized system, and some management people control everything, which increases the risk of a data breach. We propose a smart contract-based blockchain architecture for the privacy of medical health data. The key features of blockchain are immutability and user confidentiality. We use IoT applications to take advantage of blockchain technology and eliminate its disadvantages, such as low throughput, high overhead, and latency. To that end, we’re clustering the BC miners and sending data to the nearest patient cluster with various keywords and multi-functionality, it supports secure data management.
Tariq Alsboui, Hussain Al-Aqrabi, Richard Hill, Shamaila Iram
In the Internet of things (IoT), security and privacy issues are a fundamental challenge determining the successful implementation of many IoT applications. Distributed ledger technology (e.g., Blockchain) offers a great promise to solve these issues. Blockchain-based solutions support security and privacy, yet they involve significant energy due to mining, low throughput, and computational overhead that is not acceptable for IoT resource-constrained devices. In this paper, we propose an energy-efficient Privacy-Preserving Distributed Intelligence approach (PPDI) by adopting the IOTA technology. IOTA is an emerging distributed ledger technology that allows for zero fees transactions for the IoT. The proposed PPDI aims to address the privacy issues in the IoT by using the IOTA Masked Authenticated Messaging (MAM) protocol. MAM ensures privacy by encrypting and granting permission to authorized users to access data. This paper presents a healthcare scenario that demonstrate how IOTA MAM can be used to address the privacy issue in the IoT. The experimental results clearly show that IOTA MAM is a feasible solution that can be used to solve privacy related issues in the IoT domain.
Muhammad Hussain Mughal, Zaffar Ahmed Shaikh, Khurshed Ali, Safdar Ali · 5 authors
The streamflow data acquisition with various techniques and dispersing of River’s locations demand improvement of reliability and frequency aspects. One of the reliability measurement characteristics is data ownership. The authority sharing data authorizes its quality and is also responsible for the wrong decision triggered by incorrect data. The consensus-based crowdsourcing data contribute to aggregated streamflow records’ generation. The aggregated streamflow records are stored on a streamflow ledger, a Hyperledger fabric-based ledger for rivers’ streamflow data. In contrast, InterPlanery File System(IPFS) distributed file storage system is helpful for policy documents and distribution scheme storage. Blockchain-based techniques for improvement of data-intensive decision support systems. The distributed river streamflow data measured and shared by distributed gauging officials and stored on the blockchain-based distributed storage system contributes to scalability, transparency, availability, and accessibility of shareable data. All stakeholders require quality data with trust and provenance management through a persistent, linked, and immutable copy of data. This technique resolves the conflict or disagreement on streamflow optimization and flood mitigation decisions. In a nutshell, the blockchain technology with IPFS for off-chain large files storage would contribute twofold to irrigation systems and flood mitigation domains. On one side, the streamflow data aggregation has consensus from streamflow assessment for software agents and stakeholders. Secondly, the persistent data copy is shared among distributed stakeholders using IPFS-based content addressed file-sharing protocol for a common operating picture to improve effective collaboration and coordination among managers of irrigation systems and flood mitigation activities.
Multi-party business processes are based on the cooperation of different actors in a distributed setting. Blockchains can provide support for the automation of such processes, even in conditions of partial trust among the participants. On-chain data are stored in all replicas of the ledger and therefore accessible to all nodes that are in the network. Although this fosters traceability, integrity, and persistence, it undermines the adoption of public blockchains for process automation since it conflicts with typical confidentiality requirements in enterprise settings. In this paper, we propose a novel approach and software architecture that allow for fine-grained access control over process data on the level of parts of messages. In our approach, encrypted data are stored in a distributed space linked to the blockchain system backing the process execution; data owners specify access policies to control which users can read which parts of the information. To achieve the desired properties, we utilise Attribute-Based Encryption for the storage of data, and smart contracts for access control, integrity, and linking to process data. We implemented the approach in a proof-of-concept and conduct a case study in supply-chain management. From the experiments, we find our architecture to be robust while still keeping execution costs reasonably low.
Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammad Saidur Rahman, Abdelaziz Bouras
Smart manufacturing systems involve a large number of interconnected devices resulting in massive data generation. Cloud computing technology has recently gained increasing attention in smart manufacturing systems for facilitating cost-effective service provisioning and massive data management. In a cloud-based manufacturing system, ensuring authorized access to the data is crucial. A cloud platform is operated under a single authority. Hence, a cloud platform is prone to a single point of failure and vulnerable to adversaries. An internal or external adversary can easily modify users' access to allow unauthorized users to access the data. This paper proposes a role-based access control to prevent modification attacks by leveraging blockchain and smart contracts in a cloud-based smart manufacturing system. The role-based access control is developed to determine users' roles and rights in smart contracts. The smart contracts are then deployed to the private blockchain network. We evaluate our solution by utilizing Ethereum private blockchain network to deploy the smart contract. The experimental results demonstrate the feasibility and evaluation of the proposed framework's performance.
Jonathan Heiss, Robert Muth, Frank Pallas, Stefan Tai
Many service systems rely on verifiable identity-related information of their users. Manipulation and unwanted exposure of this privacy-relevant information, however, must at the same time be prevented and avoided. Peer-to-peer blockchain-based decentralization with a smart contract-based execution model and verifiable off-chain computations leveraging zero-knowledge proofs promise to provide the basis for next-generation, non-disclosing credential management solutions. In this paper, we propose a novel credential on-chaining system that ensures blockchain-based transparency while preserving pseudonymity. We present a general model compliant to the W3C verifiable credential recommendation and demonstrate how it can be applied to solve existing problems that require computational identity-related attribute verification. Our zkSNARKs-based reference implementation and evaluation show that, compared to related approaches based on, e.g., CL-signatures, our approach provides significant performance advantages and more flexible proof mechanisms, underpinning our vision of increasingly decentralized, transparent, and trustworthy service systems.
In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate as sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures thefairnessof the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.
Tahmid Hasan Pranto, Kazi Tamzid Akhter Md Hasib, Tahsinur Rahman, AKM Bahalul Haque · 6 authors
Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other hand, data-driven technologies like machine learning need authentic data to perform precisely in real-world systems. This study proposes a blockchain and smart contract-based approach to achieve robust Machine Learning (ML) algorithm for e-commerce fraud detection by facilitating inter-organizational collaboration. The proposed method uses blockchain to secure the privacy of the data. Smart contract deployed inside the network fully automates the system. An ML model is incrementally upgraded from collaborative data provided by the organizations connected to the blockchain. To incentivize the organizations, we have introduced an incentive mechanism that is adaptive to the difficulty level in updating a model. The organizations receive incentives based on the difficulty faced in updating the ML model. A mining criterion has been proposed to mine the block efficiently. And finally, the blockchain network is tested under different difficulty levels and under different volumes of data to test its efficiency. The model achieved 98.93% testing accuracy and 98.22% Fbeta score (recall-biased f measure) over eight incremental updates. Our experiment shows that both data volume and difficulty level of blockchain impacts the mining time. For difficulty level less than five, mining time and difficulty level has a positive correlation. For difficulty level two and three, less than a second is required to mine a block in our system. Difficulty level five poses much more difficulties to mine the blocks.
Trusted online credential management solutions are needed for instant and practical verification. Most of the available frameworks targeting this field violate the privacy of end-users or lack sufficient solutions in terms of security and Quality-of-Service (QoS). In this paper, we propose a Privacy-aware Fog-enhanced Blockchain-based online credential management solution, namely PriFoB. Our proposed solution adopts a public permissioned Blockchain model with different reliable encryption schemes, standardized Zero-Knowledge-Proofs (ZKPs) and Digital Signatures (DSs) within a Fog–Blockchain integrated framework, which is also GDPR compliant. We deploy both the Proof-of-Authority (PoA) and the Signatures-of-Work (SoW) consensus algorithms for efficient and secure handling of Verifiable Credentials (VCs) and global accreditation of VC issuers, respectively. Furthermore, we propose a novel three-dimensional DAG-based model of the Distributed Ledger (3DDL), and provide a ready-to-deploy PriFoB implementation. We discuss insights regarding the utilization and the potential of PriFoB, and evaluate it in terms of security, privacy, latency, throughput and power utilization. We analyze its performance in different layers of a Fog-enabled cloud architecture with simulation and emulation, and we show that PriFoB outperforms several Blockchain-based solutions utilizing Ethereum, Hyperledger Fabric, Hyperledger Besu and Hyperledger Indy platforms.