2 research outputs found
RC-chain: Reputation-based Crowdsourcing Blockchain for Vehicular Networks
As the commercial use of 5G technologies has grown more prevalent, smart
vehicles have become an efficient platform for delivering a wide array of
services directly to customers. The vehicular crowdsourcing service (VCS), for
example, can provide immediate and timely feedback to the user regarding
real-time transportation information. However, different sources can generate
spurious information towards a specific service request in the pursuit of
profit. Distinguishing trusted information from numerous sources is the key to
a reliable VCS platform. This paper proposes a solution to this problem called
"RC-chain", a reputation-based crowdsourcing framework built on a blockchain
platform (Hyperledger Fabric). We first establish the blockchain-based platform
to support the management of crowdsourcing trading and user-reputation
evaluating activities. A reputation model, the Trust Propagation \& Feedback
Similarity (TPFS), then calculates the reputation values of participants and
reveals any malicious behavior accordingly. Finally, queueing theory is used to
evaluate the blockchain-based platform and optimize the system performance. The
proposed framework was deployed on the IBM Hyperledger Fabric platform to
observe its real-world running time, effectiveness, and overall performance
Decentralized Trust Management: Risk Analysis and Trust Aggregation
Decentralized trust management is used as a referral benchmark for assisting
decision making by human or intelligence machines in open collaborative
systems. During any given period of time, each participant may only interact
with a few of other participants. Simply relying on direct trust may frequently
resort to random team formation. Thus, trust aggregation becomes critical. It
can leverage decentralized trust management to learn about indirect trust of
every participant based on past transaction experiences. This paper presents
alternative designs of decentralized trust management and their efficiency and
robustness from three perspectives. First, we study the risk factors and
adverse effects of six common threat models. Second, we review the
representative trust aggregation models and trust metrics. Third, we present an
in-depth analysis and comparison of these reference trust aggregation methods
with respect to effectiveness and robustness. We show our comparative study
results through formal analysis and experimental evaluation. This comprehensive
study advances the understanding of adverse effects of present and future
threats and the robustness of different trust metrics. It may also serve as a
guideline for research and development of next generation trust aggregation
algorithms and services in the anticipation of risk factors and mischievous
threats