Consortium blockchain management with a peer reputation system for critical information sharing

Blockchain technology based applications are emerging to establish distributed trust amongst organizations who want to share critical information for mutual benefit amongst their peers. There is a growing need for consortium based blockchain schemes that avoid issues such as false reporting and free riding that impact cooperative behavior between multiple domains/entities. Specifically, customizable mechanisms need to be developed to setup and manage consortiums with economic models and cloud-based data storage schemes to suit various application requirements. In this MS Thesis, we address the above issues by proposing a novel consortium blockchain architecture and related protocols that allow critical information sharing using a reputation system that manages co-operation amongst peers using off-chain cloud data storage and on-chain transaction records. We show the effectiveness of our consortium blockchain management approach for two use cases: (i) threat information sharing for cyber defense collaboration system viz., DefenseChain, and (ii) protected data sharing in healthcare information system viz., HonestChain. DefenseChain features a consortium Blockchain architecture to obtain threat data and select suitable peers to help with cyber attack (e.g., DDoS, Advance Persistent Threat, Cryptojacking) detection and mitigation. As part of DefenseChain, we propose a novel economic model for creation and sustenance of the consortium with peers through a reputation estimation scheme that uses 'Quality of Detection' and 'Quality of Mitigation' metrics. Similarly, HonestChain features a consortium Blockchain architecture to allow protected data sharing between multiple domains/entities (e.g., health data service providers, hospitals and research labs) with incentives and in a standards-compliant manner (e.g., HIPAA, common data model) to enable predictive healthcare analytics. Using an OpenCloud testbed with configurations with Hyperledger Composer as well as a simulation setup, our evaluation experiments for DefenseChain and HonestChain show that our reputation system outperforms state-of-the-art solutions and our consortium blockchain approach is highly scalableIncludes bibliographical references (pages 45-52)

Reliable Federated Learning for Mobile Networks

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks

CrowdBC: A blockchain-based decentralized framework for crowdsourcing

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester’s task can be solved by a crowd of workers without relying on any third trusted institution, users’ privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability and scalability of our proposed crowdsourcing system