Showing papers on "Service provider published in 2021"

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

Abstract: Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. The key idea of existing Byzantine-robust federated learning methods is that the service provider performs statistical analysis among the clients' local model updates and removes suspicious ones, before aggregating them to update the global model. However, malicious clients can still corrupt the global models in these methods via sending carefully crafted local model updates to the service provider. The fundamental reason is that there is no root of trust in existing federated learning methods. In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. In particular, the service provider itself collects a clean small training dataset (called root dataset) for the learning task and the service provider maintains a model (called server model) based on it to bootstrap trust. In each iteration, the service provider first assigns a trust score to each local model update from the clients, where a local model update has a lower trust score if its direction deviates more from the direction of the server model update. Then, the service provider normalizes the magnitudes of the local model updates such that they lie in the same hyper-sphere as the server model update in the vector space. Our normalization limits the impact of malicious local model updates with large magnitudes. Finally, the service provider computes the average of the normalized local model updates weighted by their trust scores as a global model update, which is used to update the global model. Our extensive evaluations on six datasets from different domains show that our FLTrust is secure against both existing attacks and strong adaptive attacks.

Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

Abstract: Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design, and shows the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.

B-Ride: Ride Sharing With Privacy-Preservation, Trust and Fair Payment Atop Public Blockchain

Abstract: Ride-sharing is a service that enables drivers to share trips with other riders, contributing to appealing benefits of shared travel cost and reducing traffic congestion. However, the majority of existing ride-sharing services rely on a central third party to organize the service, which make them subject to a single point of failure and privacy disclosure concerns by both internal and external attackers. Moreover, they are vulnerable to distributed denial of service (DDoS) and Sybil attacks launched by malicious users and external attackers. Besides, high service fees are paid to the ride-sharing service provider. In this paper, we propose a decentralized ride-sharing service based on public Blockchain, named B-Ride. B-Ride enables drivers to offer ride-sharing services without relying on a trusted third party. Both riders and drivers can learn whether they can share rides while preserving their trip data, including pick-up/drop-off location, departure/arrival date and travel price. However, malicious users exploit the anonymity provided by the public blockchain to submit multiple ride requests or offers, while not committing to any of them, in order to find a better offer or to make the system unreliable. B-Ride solves this problem by introducing a time-locked deposit protocol for a ride-sharing by leveraging smart contract and zero-knowledge set membership proof. In a nutshell, both a driver and a rider have to show their good will and commitment by sending a deposit to the blockchain. Later, a driver has to prove to the blockchain on the agreed pick-up time that he/she arrived at the pick-up location on time. To preserve rider/driver privacy by hiding the exact pick-up location, the proof is performed using zero-knowledge set membership proof. Moreover, to ensure fair payment, a pay-as-you-drive methodology is introduced based on the elapsed distance of the driver and rider. In addition, we introduce a reputation model to rate drivers based on their past behaviour without involving any third-parties to allow riders to select them based on their history on the system. Finally, we implement our protocol and deploy it in a test net of Ethereum. The experimental results show the applicability of our protocol atop existing real-world blockchains.

Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System

Abstract: We design a new blockchain-based access control protocol in IoT-enabled smart-grid system, called DBACP-IoTSG Through the proposed DBACP-IoTSG, the data is securely brought to the service providers from their respective smart meters (SMs) The peer-to-peer (P2P) network is formed by the participating service providers, where the peer nodes are responsible for creating the blocks from the gathered data securely from their corresponding SMs and adding them into the blockchain after validation of the blocks using the voting-based consensus algorithm In our work, the blockchain is considered as private because the data collected from the consumers of the SMs are private and confidential By the formal security analysis under the random oracle model, nonmathematical security analysis and software-based formal security verification, DBACP-IoTSG is shown to be resistant against various attacks We carry out the experimental results of various cryptographic primitives that are needed for comparative analysis using the widely used multiprecision integer and rational arithmetic cryptographic library (MIRACL) A detailed comparative study reveals that DBACP-IoTSG supports more functionality features and provides better security apart from its low communication and computation costs as compared to recently proposed relevant schemes In addition, the blockchain implementation of DBACP-IoTSG has been performed to measure computational time needed for the varied number of blocks addition and also the varied number of transactions per block in the blockchain

Outsourcing Service Fair Payment Based on Blockchain and Its Applications in Cloud Computing

Abstract: As a milestone in the development of outsourcing services, cloud computing enables an increasing number of individuals and enterprises to enjoy the most advanced services from outsourcing service providers. Because online payment and data security issues are involved in outsourcing services, the mutual distrust between users and service providers may severely impede the wide adoption of cloud computing. Nevertheless, most existing solutions only consider a specific type of services and rely on a trusted third-party to realize fair payment. In this paper, to realize secure and fair payment of outsourcing services in general without relying on any third-party, trusted or not, we introduce BPay, an outsourcing service fair payment framework based on blockchain in cloud computing. We first propose the system architecture, adversary model and design goals of BPay, then describe the design details. Our security and compatibility analysis indicates that BPay achieves soundness and robust fairness and it is compatible with the Bitcoin blockchain and the Ethereum blockchain. The key to the robust fairness and compatibility lies in an all-or-nothing checking-proof protocol and a top-down checking method. In addition, our experimental results show that BPay is computationally efficient. Finally, we present the applications of BPay in outsourcing services.

A Blockchain-Enabled Deduplicatable Data Auditing Mechanism for Network Storage Services

Abstract: Since network storage services achieve widespread adoption, security and performance issues are becoming primary concerns, affecting the scalability of storage systems Countermeasures like data auditing mechanisms and deduplication techniques are widely studied However, the existing data auditing mechanism with deduplication cannot solve the problems such as high cost and reliance on trusted third parties in traditional approaches, and it also faces the problem of repeated auditing of data shared by multiple-tenant This article proposes a blockchain-based deduplicatable data auditing mechanism We first design a client-side data deduplication scheme based on bilinear-pair techniques to reduce the burden on users and service providers On this basis, we achieve a trustworthy and efficient data auditing mechanism that helps to check data integrity by using both the blockchain technique and bilinear pairing cryptosystem The blockchain system is used to record the behaviors of entities in both data outsourcing and auditing processes so that the corresponding immutable records can be used to not only ensure the credibility of audit results but also help to monitor unreliable third-party auditors Finally, theoretical analysis and experiments reveal the effectiveness and performance of our scheme

EIHDP: Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

Abstract: Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

Blockchain-Enabled Accountability Mechanism Against Information Leakage in Vertical Industry Services

Abstract: The emergence of 5 G technology contributes to create more open and efficient eco-systems for various vertical industries. Especially, it significantly improves the capabilities of the vertical industries focusing on content-sharing services like mobile telemedicine, etc. However, cyber threats such as information leakage or piracy are more likely to occur in an open 5 G networks. So tracking information leakage in 5 G environments has become a daunting task. The existing tracing and accountability schemes have nonnegligible limitations in practice due to the dependence on a Trusted Third Party (TTP) or being encumbered with the significant overhead. Fortunately, the blockchain helps to mitigate these problems. In this paper, we propose a blockchain-enabled accountability mechanism against information leakage in the content-sharing services of the vertical industry services. For any information converted to vector form, we use the blockchain technology to ensure that service providers and clients can securely and fairly generate and share watermarked content. Besides, the homomorphic encryption is introduced to avoid the disclosure of the watermarking content, which guarantees the subsequent TTP-free arbitration. Finally, we theoretically analyze the security of the scheme and verify its performance.

On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors

Abstract: We study how delivery data can be applied to improve the on-time performance of last-mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we di...

Multi class SVM algorithm with active learning for network traffic classification

Abstract: With the current massive amount of traffic that is going through the internet, internet service providers (ISPs) and networking service providers (NSPs) are looking for various ways to accurately predict the application type of flow that is going through the internet. Such prediction is critical for security and network monitoring applications as they require application type to be known in prior. Traditional ways using port-based or payload-based analysis are not sufficient anymore as many applications start using dynamic unknown port numbers, masquerading, and encryption techniques to avoid being detected. Recently, machine learning has gained significant attention in many prediction applications including traffic classification from flow features or characteristics. However, such algorithms suffer from an imbalanced data problem where some applications have fewer flow data and hence difficult to predict. In this paper, we employ network flow-level characteristics to identify the application type of traffic. Furthermore, we propose the use of an improved support vector machine (SVM) algorithm, named cost-sensitive SVM (CMSVM), to solve the imbalance problem in network traffic identification. CMSVM adopts a multi-class SVM algorithm with active learning which dynamically assigns a weight for applications. We examine the classification accuracy and performance of the CMSVM algorithm using two different datasets, namely MOORE_SET and NOC_SET datasets. Our results show that the CMSVM algorithm can reduce computation cost, improve classification accuracy and solve the imbalance problem when compared to other machine learning techniques.

COVID-19 Networking Demand: An Auction-based Mechanism for Automated Selection of Edge Computing Services

Abstract: Network and cloud service providers are facing an unprecedented challenge to meet the demand of end-users during the COVID-19 pandemic. Currently, billions of people around the world are ordered to stay at home and use remote connection technologies to prevent the spread of the disease. The COVID-19 crisis brought a new reality to network service providers that will eventually accelerate the deployment of edge computing resources to attract the massive influx of users' traffic. The user can elect to procure its resource needs from any edge computing provider based on a variety of attributes such as price and quality. The main challenge for the user is how to choose between the price and multiple quality of service deals when such offerings are changing continually. This problem falls under multi-attribute decision-making. This paper investigates and proposes a novel auction mechanism by which network service brokers would be able to automate the selection of edge computing offers to support their end-users. We also propose a multi-attribute decision-making model that allows the broker to maximize its utility when several bids from edge-network providers are present.The evaluation and experimentation show the practicality and robustness of the proposed model.

Ethical issues in service robotics and artificial intelligence

Abstract: As we come to increasingly rely on robotic and Artificial Intelligence technologies, there are a growing number of ethical concerns to be considered by both service providers and consumers. This re...

A Blockchain-Based Multi-Cloud Storage Data Auditing Scheme to Locate Faults

Abstract: Network storage services have benefited countless users worldwide due to the notable features of convenience, economy and high availability. Since a single service provider is not always reliable enough, more complex multi-cloud storage systems are developed for mitigating the data corruption risk. While a data auditing scheme is still needed in multi-cloud storage to help users confirm the integrity of their outsourced data. Unfortunately, most of the corresponding schemes rely on trusted institutions such as the centralized third-party auditor (TPA) and the cloud service organizer, and it is difficult to identify malicious service providers after service disputes. Therefore, we present a blockchain-based multi-cloud storage data auditing scheme to protect data integrity and accurately arbitrate service disputes. We not only introduce the blockchain to record the interactions among users, service providers, and organizers in data auditing process as evidence, but also employ the smart contract to detect service dispute, so as to enforce the untrusted organizer to honestly identify malicious service providers. We also use the blockchain network and homomorphic verifiable tags to achieve the low-cost batch verification without TPA. Theoretical analyses and experiments reveal that the scheme is effective in multi-cloud environments and the cost is acceptable.

Optimal Remanufacturing Service Resource Allocation for Generalized Growth of Retired Mechanical Products: Maximizing Matching Efficiency

Abstract: Maximizing the residual value of retired products and reducing process consumption and resource waste are vital for Generalized Growth-oriented Remanufacturing Services (GGRMS). Under the GGRMS, the traditional product-oriented remanufacturing methods to be changed: the products in GGRMS should be divided into multiple parts for maximizing residual value of different parts. However, this increases the difficulty of resource matching for service activities. To improve the efficiency of resource matching, we first used rough-fuzzy number and structural entropy weighting method to perform a coupling analysis on all service activities in the generalized growth scheme set, and to merge redundant service activities. We then considered the interests of both the service providers and integrators and added flexible impact factors to establish a service resource optimization configuration model, and solved it with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, we, using a retired manual gearbox an experiment, optimized the service resource allocation for its generalized growth scheme set. The experimental results shown that the overall matching efficiency was increased by 74.56% after merging redundant service activities, showing that the proposed method is suitable for the resource allocation of the generalized growth for complex single mechanical products, and can offer guidelines to the development of RMS.

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Abstract: A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics

Abstract: The rapid development of urbanisation and the ever-changing consumers’ demands are constantly changing the urban logistics industry, imposing challenges on logistics service providers to improve cu...

Sustainability of Healthcare Data Analysis IoT-based Systems using Deep Federated Learning

Abstract: Due to recent privacy trends, and the increase in data breaches in various industries, it has become imperative to adopt new technologies that support data privacy, maintain accuracy, and ensure sustainability at the same time. The healthcare industry is one of the most vulnerable sectors to cyber-attacks and data breaches as health data is highly sensitive and distributed in nature. The use of IoT devices with machine learning models to monitor health status has made the challenge more challenging, as it increases the distribution of health data and adds a decentralized structure to healthcare systems. A new privacy-preserving technology, namely, federated learning, is promising for such a challenge as implementing solutions that integrate federated learning with deep learning, for healthcare applications that rely on IoT, provides several benefits by mainly preserving data privacy, building robust and high accuracy models, and dealing with the decentralized structure, thus achieving sustainability. This article proposes a Deep Federated Learning framework for healthcare data monitoring and analysis using IoT devices. Moreover, it proposes a federated learning algorithm that addresses the local training data acquisition process. Furthermore, it presents an experiment to detect skin diseases using the proposed framework. The extensive results collected show that the deep federated learning models can preserve data privacy without sharing it, maintain the decentralized structure of the system made by IoT devices, improve the area under the curve (AUC) of the model to reach 97%, and reduce the operational costs for service providers.

COVID-19 and the pursuit of supply chain resilience: reactions and “lessons learned” from logistics service providers (LSPs)

Abstract: Purpose: The purpose of this paper is to provide new insights into the reactions and lessons learned with regard to the COVID-19 pandemic in terms of how logistics service providers (LSPs) managed to maintain supply chains resilience and what focus areas have been changed to keep operations functional and uphold financial stability. Design/methodology/approach: Based on data-gathering techniques in interpretive research this study collected primary data via semi-structured interviews, interviewing informants from selected LSPs that operate on a global scale. Findings: The results show that LSPs have built their reactions and actions to the COVID-19 outbreak around five main themes: “create revenue streams,” “enhance operational transport flexibility,” “enforce digitalization and data management,” “optimize logistics infrastructure” and “optimize personnel capacity.” These pillars build the foundation to LSP resilience that enables supply chains to stay resilient during an external shock of high impact and low probability. Originality/value: The results of this study provide insights into how LSPs have managed the downsides and found innovative ways to overcome operational and financial challenges during the COVID-19 outbreak. As one of the first studies that specially focuses on the role of LSPs during the COVID-19 pandemic, this study categorizes the LSPs’ reactions and provides a “lessons learned” framework from a managerial perspective. From a theoretical perspective, this paper discusses the strategic role of LSPs in supply chain management and thereby extends current supply chain literature with a focus on LSP resilience. © 2021, David M. Herold, Katarzyna Nowicka and Aneta Pluta-Zaremba and Sebastian Kummer.