Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne · 6 authors
The combination of mobile edge computing (MEC) and blockchain is transforming the current computing services in Internet of Things networks, by offering task offloading solutions with security enhancement enabled by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme to optimize the system utility in blockchain-empowered MEC. Herein, each edge device (ED) not only handles data tasks but also deals with block mining which makes the system design and optimization highly complex. Therefore, we develop a novel cooperative deep reinforcement learning (DRL) approach which allows EDs to cooperatively offload their data tasks to the MEC server and perform block mining based on a Proof-of-Reputation consensus mechanism. Simulation results demonstrate that the proposed scheme significantly improves offloading utility, reduces blockchain mining latency, and achieves better system utility, compared to other non-cooperative and cooperative schemes.
The Internet of Things (IoT) revolution has introduced sensor-rich devices to an ever growing landscape of smart environments. A key component in the IoT scenarios of the future is the requirement to utilize a shared database that allows all participants to operate collaboratively, transparently, immutably, correctly and with performance guarantees. Blockchain databases have been proposed by the community to alleviate these challenges, however existing blockchain architectures suffer from performance issues. In this short paper we propose Triastore, a novel permissioned blockchain database system that carries out machine learning on the edge, abstracts machine learning models into primitive data blocks that are subsequently stored and retrieved from the blockchain. Triastore comprises of two internal routines, namely: (i) Proof of Federated Learning (PoFL), which trains in a distributed manner a global model for the ingested data; and (ii) Blockchain Consensus, which commits this generated model data on permissioned blockchain database. We present a detailed explanation of our data ingestion algorithm with relevant examples and carry out an experimental evaluation with image data from MNIST. The evaluation shows that our proposed data ingestion framework retains high levels of accuracy with low loss in data quality.
With the rapid development of sensors and IoT technology, personal health data can be collected and stored by various wearable devices and utilized for healthcare. To share and use sensitive health data securely and efficiently, a variety of solutions based on blockchain have been proposed and developed. However, there are still many issues to be solved, such as how to let individuals control and manage their own data, and how to make all data accesses strictly auditable. In this paper, we present a new model of Individual-Initiated Auditable Access Control (IIAAC) enabled with blockchain, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and IPFS (InterPlanetary File System). After introducing scenarios for sharing and use of health data, we define the design requirements for a blockchain-based system and describe the basic system architecture. We discuss the detailed procedures in IIAAC, including CP-ABE key generation, data publication and data retrieval. We further compare this study with related work in terms of functions and features.
The vehicular ad-hoc networks (VANETs) are considered a key mechanism for the collection and dissemination of basic safety messages (BSM) in the modern transportation system. However, the presence of compromised or malicious vehicles within the network can disrupt the security of the information and the safety of the passengers. The emergence of a blockchain-based distributed framework in VANETs ensures transparency and security within the network without the assist of a trusted centralized entity. Nonetheless, the presence of the majority of malicious vehicles within the region of interest (ROI) can still bypass the security provided by the state-of-the-art blockchain-based frameworks. In this paper, we propose a Blockchain-assisted Misbehavior Detection and Event Validation (BLAME) framework that can effectively detect the valid traffic events and the malicious vehicles from the ROI by leveraging the neighbor information and the event recorded by the individual vehicles even if they are in majority. The efficacy of BLAME has been validated through simulations in VENTOS simulators and a simulated blockchain environment by extensively addressing different use case scenarios.
Jingwei Liu, Xinyu He, Rong Sun, Xiaojiang Du · 5 authors
Each bank has different clients and each client may have transactions with multiple banks. Hence, clients’ data in a single bank may be partial and incomplete. If the data can be combined, each bank obtains comprehensive information, so as to better carry out business and enhance the quality of service, such as recommending financial products and inquiring about personal credit records. However, after the promulgation of GDPR by European Union in 2018, it is illegal to directly consolidate data crossing enterprises due to privacy and security concerns, especially for privacy-sensitive industries. Emerging federated learning(FL) is very suitable for secure data sharing for distributed banks in privacy. To prevent from connection of clients’ data and the certain bank, we adopt anonymity mechanism to hide the real identity of banks. In this paper, we first propose blockchain-empowered secure federated learning for distributed banks based on multi-party computation(MPC) with multi-key fully-homomorphic encryption(FHE) scheme. Then, we give detailed description of multi-key FHE based MPC protocol, anonymity mechanism and permissioned blockchain consensus protocol. Finally, we analyze the security and compare our scheme with several existed schemes. Numerical results show that the proposed data sharing scheme has good performance in terms of computational overhead and model accuracy.
Jingwei Liu, Mingli Wu, Rong Sun, Xiaojiang Du · 5 authors
In recent years, more and more medical institutions have been using electronic medical records (EMRs) to improve service efficiency and reduce storage cost. However, it is difficult for medical institutions with different management methods to share medical data. The medical data of patients is easy to be abused, and there are security risks of privacy data leakage. The above problems seriously impede the sharing of medical data. To solve these problems, we propose a blockchain-based medical data sharing scheme with attribute-based searchable encryption, named BMDS. In BMDS, encrypted EMRs are securely stored in the interplanetary file system (IPFS), while corresponding indexes and other information are stored in a medical consortium blockchain. The proposed BMDS has the features of tamper-proof, privacy preservation, verifiability and secure key management, and there is no single point of failure. The performance evaluation of computational overhead and security analysis show that the proposed BMDS has more comprehensive security features and practicability.
Proposes a block chain-based bidding solution. By recording all bidding processes, all quotation information uses additive homomorphism and multiplicative homomorphism encryption, combined with the application of random numbers, to prevent the encryption party from deducing other participating party's information. Using the homomorphic encryption-based blockchain electronic bidding scheme proposed in this article, using a decentralized solution, protects the security of bidders, improves the fairness and reliability of bidding, and the bidding process can be traced, and block chain ledger is stored distributed at every node of the blockchain, to prevent hackers from attacking a centralized node to arbitrarily tamper or illegally obtain bidding data, and the security is greatly improved.
Electronic voting can greatly reduce the cost of voting activities and solve many problems of voting information leakage caused by traditional paper voting. As the development and extension of blockchain technology, Ethereum technology can well solve the centralization problem of traditional electronic voting. This solution proposes an electronic voting system based on Ethereum technology to meet the security requirements of electronic voting, and uses Ethereum smart contract technology to replace the traditional trusted third party, which solves the possible drawbacks of the centralized voting system and improves Security and voters' trust in the voting system reduce voting costs. Finally, the electronic voting function is realized, and the normal operation of the system is guaranteed through the test.
Smart contracts present a uniform approach for deploying distributed computation and have become a popular means to develop security critical applications. A major barrier to adoption for many applications is the public nature of existing systems, such as Ethereum. Several systems satisfying various definitions of privacy and requiring various trust assumptions have been proposed; however, none achieved the universality and uniformity that Ethereum achieved for non-private contracts: One unified method to construct most contracts. We provide a unified security model for private smart contracts which is based on the Universal Composition (UC) model and propose a novel core protocol, KACHINA, for deploying privacy-preserving smart contracts, which encompasses previous systems. We demonstrate the KACHINA method of smart contract development, using it to construct a contract that implements privacy-preserving payments, along the lines of Zerocash, which is provably secure in the UC setting and facilitates concurrency.
We propose an ideal functionalityFCDand a construction ΠCDfor oblivious and updatable committed databases.FCDallows a proverPto read, write, and update values in a database and to prove to a verifierVin zero-knowledge (ZK) that a value is read from or written into a certain position. The following properties must hold: (1) values stored in the database remain hidden fromV; (2) a value read from a certain position is equal to the value previously written into that position; (3) (obliviousness) both the value read or written and its position remain hidden fromV.ΠCDis based on vector commitments. After the initialization phase, the cost of read and write operations is independent of the database size, outperforming other techniques that achieve cost sublinear in the dataset size for prover and/or verifier. Therefore, our construction is especially appealing for large datasets. In existing “commit-and-prove” two-party protocols, the task of maintaining a committed database betweenPandVand reading and writing values into it is not separated from the task of proving statements about the values read or written.FCDallows us to improve modularity in protocol design by separating those tasks. In comparison to simply using a commitment scheme to maintain a committed database,FCDallowsPto hide efficiently the positions read or written fromV. Thanks to this property, we design protocols for e.g. privacy-preserving e-commerce and location-based services whereVgathers aggregate statistics about the statements thatPproves in ZK.
The next generation wireless network will be a heterogeneous network with multiple access networks, and Mobile Edge Computation (MEC) is oriented to heterogeneous users with various security authentication mechanisms. However, compared to the central servers, the lightweight and the limited computing power make MEC servers hard to support multiple security authentication mechanisms at the same time. To resolve this issue, this paper introduces a blockchain based unified authentication with Zero-knowledge Proof (ZPB authentication) system. Blockchain is adopted to recorder the authentication results, so there is no need to perform secondary authentication in HetNet handoff. A non-interactive Schnorr Zero-knowledge Proof scheme is used to avoid privacy issues caused by blockchain, and it can calculate parameters in advance to reduce real-time overhead of MEC. At the same time, the mechanism of anonymous code updating periodically also makes the system more secure. Through performance analysis and simulation, it can be seen that the authentication system proposed in this paper can achieve both security and effectiveness, and is suitable for HetNet access authentication in MEC scenario.
Xiumei Deng, Jun Li, Chuan Ma, Kang Wei · 8 authors
The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [$\mathcal{O}(1/V)$, $\mathcal{O}(\sqrt{V})$] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter $V$. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption.
Recent developments in collection, computation and communication have expanded the way of data reporting in intelligent Internet of Things (IoT). However, diversity and complexity of data sources also impose new trust challenge in data collection process since untrust reporters tend to report false or even malicious data, which highlights the need to develop a novel methodology to solve such challenge. Thus, based on this domain, inspired by deterrence theory, this article proposes a blockchain-based trust system with assistant of drones to deter malicious data reporting in intelligent IoT. Specifically, to deter malicious data reporting, based on the blockchain technology, the data sensed by fully trusted drones is public published on blockchain showing participants the data standards, named as malicious deterrence scheme. This scheme provides a barrier for malicious reporters to arbitrarily publish false data to blockchain, since the false data can be easily detected while they cannot deny. Second, to further reduce malicious data reporting, a strict penalty mechanism is proposed to punish malicious reporters who have reported false data to blockchain to reduce the malicious data reporting in the following task through punishment. Third, note that the sensing of data standard generates additional costs, therefore, a drone flight route scheme based on a simper deep reinforcement learning with multihead attention mechanism (MA-DRL) is designed to reduce the flight distance for drones. Finally, extensive experiments demonstrate efficiency of our proposed system in terms of reducing malicious data reporting in advance as well as reducing drone flight distance.
With the advent of the Internet of Things (IoT), crowdsensing, as a new emerging application of the IoT that employs ubiquitous mobile users with smartphones for data collection and processing, has further deepened our knowledge. However, the problems of the current crowdsensing systems regarding system security, user privacy, and user payment (UP) raise serious privacy and security concerns, which affect participants’ adoption of the system. The Blockchain technology allows for nondeterministic multiple parties to interact with each other anonymously in a network that is not fully trusted. In this article, we propose a new decentralized crowdsensing system, calledCrowdHB. Unlike other blockchain-based crowdsensing systems,CrowdHBadopts a hybrid blockchain architecture and uses smart contracts to achieve location privacy preservation and ensure data quality while improving the system performance. Furthermore, to optimize task assignments to mobile users, we propose a location privacy-preserving optimization mechanism (LPPOM) and the approach of consistency optimization (ACO) to achieve a tradeoff between user privacy and system performance. The extensive experimental results show that the proposedCrowdHBoutperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.
Ultra-dense edge computing is expected to provide delay-sensitive and computational-intensive services for mobile devices. Due to the complexity and unpredictability of the network environment, it is challenging to ensure the continuity and security of computing offloading services in the process of user movement. Most existing works consider the decisions of communication handover and computational offloading simultaneously while ignoring the security on offloading tasks. In light of this, we propose a secure mobility management framework for blockchain-based ultra-dense edge computing, where blockchain reduces duplicate authentication between edge servers. We jointly optimize the wireless handover and service migration decisions between base stations, which is translated into a multi-objective dynamic optimization problem using the Lyapunov optimization. The optimization problem is solved by deep reinforcement learning approach based on theActor–Criticmethod. Finally, we use simulation studies to evaluate the performance of the proposed scheme. The results show that, compared with other existing schemes, the proposed scheme can reduce the average delay of computing tasks, the rate of tasks failure and the rate of handover.
With the development of wireless communication technology and the automobile industry, the Vehicular Ad Hoc Networks bring many conveniences to humans in terms of safety and entertainment. In the process of communication between the nodes, security problems are the main concerns. Blockchain is a decentralized distributed technology used in nonsecure environments. Using blockchain technology in the VANETs can solve the security problems. However, the characteristics of highly dynamic and resource-constrained VANETs make the traditional chain blockchain system not suitable for actual VANETs scenarios. Therefore, this paper proposes a lightweight blockchain architecture using DAG-lattice structure for VANETs, called V-Lattice. In V-Lattice, each node (vehicle or roadside unit) has its own account chain. The transactions they generated can be added to the blockchain asynchronously and parallelly, and resource-constrained vehicles can store the pruned blockchain and execute blockchain related operations normally. At the same time, in order to encourage more nodes to participate in the blockchain, a reputation-based incentive mechanism is introduced in V-Lattice. This paper uses Colored Petri Nets to verify the security of the architecture and verifies the feasibility of PoW anti-spam through experiment. The validation results show that the architecture proposed in this paper is security, and it is feasible to prevent nodes from generating malicious behaviors by using PoW anti-spam.
The rising of digital economy is due to the rapidly development of the new generation of information technology, represented by Internet of Things (IoT) technology. However, the huge number of sensors have limited resources and lack robust security mechanism, which brings a great risk challenges for centralized access control system. In order to deal with these challenges, the paper proposes a novel Capability-Based Access Control Model (NCBAC), which makes use of the advantages of Capability-Based Access Control (CBAC) decision-making mechanism and introduces role sets and attribute set for smart contract. This model is built for providing a decentralized, flexible, expandable and high-granularity access control system. Additionally, token has been conducted in access control model for enhancing the system's capability. Finally, the simulation experiment results showed the feasibility and effectiveness of the system, which also demonstrates the token mechanism promoting the access control performance of the system effectively.
Abdelwahab Boualouache, Hichem Sedjelmaci, Thomas Engel
Privacy is a key requirement for connected vehicles. Cooperation between vehicles is mandatory for achieving location privacy preservation. However, non-cooperative vehicles can be a big issue to achieve this objective. To this end, we propose a novel monetary incentive scheme for cooperative location privacy preservation in 5G-enabled Vehicular Fog Computing. This scheme leverages a consortium blockchain-enabled fog layer and smart contracts to ensure a trusted and secure cooperative Pseudonym Changing Processes (PCPs). We also propose optimized smart contracts to reduce the monetary costs of vehicles while providing more location privacy preservation. Moreover, a resilient and lightweight Utility-based Delegated Byzantine Fault Tolerance (U-DBFT) consensus protocol is proposed to ensure fast and reliable block mining and validation. The performance analysis shows that our scheme has effective incentive techniques to stimulate non-cooperative vehicles and provides optimal monetary cost management and secure, private, fast validation of blocks.
Machine learning has become ubiquitous across many fields in the last decade and modern real world applications often require a decentralized solution for training such models. This demand sprouted the research in federated learning, which solves some of the challenges with centralized machine learning, but at the same times raises further questions in regard to security, privacy and scalability. We have designed and implemented DecFL, an ubiquitous protocol for decentralized model training. The protocol is machine-learning-model-, vendor-, and technology-agnostic and provides a basis for practitioner's own implementations. The implemented DecFL framework presented in this article is an exemplary realization of the carefully designed protocol stack based on Ethereum and IPFS and offers a scalable baseline solution for decentralized machine learning. In this article, we present a study based on the proposed protocol, its theoretical bounds and experiments based on the implemented framework. Using open-source datasets (MNIST and CIFAR10), we demonstrate key features, the actual cost of training a model (in euro) and the communication overhead. We further show that through a proper choice of technologies DecFL achieves a linear scaling, which is a non-trivial task in a decentralized setting. Along with discussing some of the security challenges in the field, we highlight aggregation poisoning as a relevant attack vector, its associated risks and a possible prevention strategy for decentralized model training through DecFL.
ZK (zero knowledge) proof of knowledge of discrete logarithm (and sometimes extended to ZK proof of equality of discrete logarithms) in cyclic groups with unknown orders are widely employed in various cryptographic applications. To the best of our knowledge the present implementations of these two proofs have some drawbacks. Firstly, they can only achieve statistical ZK, which is not only weaker in theory than perfect ZK but also difficult to formally prove in practice. Moreover, the drawback is not limited to theoretic problems like provability but sometimes deteriorate efficiency of ZK proof to an intolerable level as we will show in a case study. The first perfect ZK argument of the proof is proposed in this paper, which is formally provable and can always guarantee acceptable efficiency. It is especially suitable for applications with high requirement on privacy and complex secure protocols requiring concise and formal proof of ZK privacy.
Electronic health records (EHRs) are important assets of the healthcare system and should be shared among medical practitioners to improve the accuracy and efficiency of diagnosis. Blockchain technology has been investigated and adopted in healthcare as a solution for EHR sharing while preserving privacy and security. Blockchain can revolutionize the healthcare system by providing a decentralized, distributed, immutable, and secure architecture. However, scalability has always been a bottleneck in blockchain networks due to the consensus mechanism and ledger replication to all network participants. Sharding helps address this issue by artificially partitioning the network into small groups termed shards and processing transactions parallelly while running consensus within each shard with a subset of blockchain nodes. Although this technique helps resolve issues related to scalability, cross-shard communication overhead can degrade network performance. This study proposes a transaction-based sharding technique wherein shards are formed on the basis of a patient’s previously visited health entities. Simulation results show that the proposed technique outperforms standard-based healthcare blockchain techniques in terms of the number of appointments processed, consensus latency, and throughput. The proposed technique eliminates cross-shard communication by forming complete shards based on “the need to participate” nodes per patient.
Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (like finance, healthcare), training a GNN model centrally faces challenges due to the distributed data silos. Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training. We term it as federated graph learning (FGL). Although FGL has received increasing attention recently, the definition and challenges of FGL is still up in the air. In this position paper, we present a categorization to clarify it. Considering how graph data are distributed among clients, we propose four types of FGL: inter-graph FL, intra-graph FL and graph-structured FL, where intra-graph is further divided into horizontal and vertical FGL. For each type of FGL, we make a detailed discussion about the formulation and applications, and propose some potential challenges.
Sidra Malik, Naman Gupta, Volkan Dedeoglu, Salil S. Kanhere · 5 authors
Blockchain technology can provide immutability, provenance and traceability in supply chains. To utilize Blockchain's full potential, it is important to link supply chain events to the relevant entities for traceability and accountability purposes. Authorized participation is realised through consortium of various organisations. Transactions are verified by peer nodes pertaining to the consortium. Hence, privacy preservation of trade sensitive information such as trade flows and locations of production, storage and retail sites cannot be ascertained. In this work, we propose a privacy-preservation framework, TradeChain, which decouples the trade events of participants using decentralised identities. TradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the following novel contributions: a) it incorporates two separate ledgers: a public permissioned blockchain for maintaining identities and the permissioned blockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs) on traders' private credentials to prove multiple identities on trade ledger and c) allows data owners to define dynamic access rules for verifying traceability information from the trade ledger using access tokens and Ciphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept implementation of TradeChain is presented on Hyperledger Indy and Fabric and an extensive evaluation of execution time, latency and throughput reveals minimal overheads.