Multi-FedLS: a Framework for Cross-Silo Federated Learning Applications on Multi-Cloud Environments

Federated Learning (FL) is a distributed Machine Learning (ML) technique that can benefit from cloud environments while preserving data privacy. We propose Multi-FedLS, a framework that manages multi-cloud resources, reducing execution time and financial costs of Cross-Silo Federated Learning applications by using preemptible VMs, cheaper than on-demand ones but that can be revoked at any time. Our framework encloses four modules: Pre-Scheduling, Initial Mapping, Fault Tolerance, and Dynamic Scheduler. This paper extends our previous work \cite{brum2022sbac} by formally describing the Multi-FedLS resource manager framework and its modules. Experiments were conducted with three Cross-Silo FL applications on CloudLab and a proof-of-concept confirms that Multi-FedLS can be executed on a multi-cloud composed by AWS and GCP, two commercial cloud providers. Results show that the problem of executing Cross-Silo FL applications in multi-cloud environments with preemptible VMs can be efficiently resolved using a mathematical formulation, fault tolerance techniques, and a simple heuristic to choose a new VM in case of revocation.Comment: In review by Journal of Parallel and Distributed Computin

DECENTRALIZED SOCIAL NETWORK SERVICE USING THE WEB HOSTING SERVER FOR PRIVACY PRESERVATION

In recent years, the number of subscribers of the social network services such as Facebook and Twitter has increased rapidly. In accordance with the increasing popularity of social network services, concerns about user privacy are also growing. Existing social network services have a centralized structure that a service provider collects all the user’s profile and logs until the end of the connection. The information collected typically useful for commercial purposes, but may lead to a serious user privacy violation. The user’s profile can be compromised for malicious purposes, and even may be a tool of surveillance extremely. In this paper, we remove a centralized structure to prevent the service provider from collecting all users’ information indiscriminately, and present a decentralized structure using the web hosting server. The service provider provides only the service applications to web hosting companies, and the user should select a web hosting company that he trusts. Thus, the user’s information is distributed, and the user’s privacy is guaranteed from the service provider

Review on IoT Security and Challenge in Industry 4.0

Wireless networks are very exposed to the danger of security. The majority of in military, commercial, health, retail, and transportation wireless communication network is used. These systems utilize networks that are wired, mobile, or adhoc. The Internet of Things (IoT) was quite attractive. The future of the Internet is regarded by IoT. In the future, IoT plays an important part and affects our way of life, norms, and business methods. IoT use is predicted to expand quickly in the next years in many applications. The IoT provides for the connection and information sharing of billions of equipment, people, and services. As IoT devices are being used more widely, several security threats are occurring in the IoT networks. In order to provide privacy, authentication, access, and integrity control, it is crucial to implement efficient protocols for the security of IoT networks and privacy among others. In addition, user privacy in the IoT environment is becoming critical since much personal information is provided and distributed among related items. It is, therefore, necessary to guarantee that personal data are protected and controlled from cloud events. The presentation addresses security and privacy dangers and concerns coming out of IoT services and presents ways to the industrial problem of security and privacy. In this article, a study on security and problems in IoT networks are discussed

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

On participatory service provision at the network edge with community home gateways

Edge computing is considered as a technology to enable new types of services which operate at the network edge. There are important use cases in ambient intelligence and the Internet of Things (IoT) for edge computing driven by huge business potentials. Most of today's edge computing platforms, however, consist of proprietary gateways, which are either closed or fairly restricted to deploy any third-party services. In this paper we discuss a participatory edge computing system running on home gateways to serve as an open environment to deploy local services. We present first motivating use cases and review existing approaches and design considerations for the proposed system. Then we show our platform which materializes the principles of an open and participatory edge environment, to lower the entry barriers for service deployment at the network edge. By using containers, our platform can flexibly enable third-party services, and may serve as an infrastructure to support several application domains of ambient intelligence.Peer ReviewedPostprint (author's final draft

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions