FairCs-Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment.

Area of Science:

• Computer Science
• Distributed Systems

Background:

• Blockchain systems offer decentralized alternatives for crowdsensing, managing data quality, payments, and storage.
• Smart contracts in blockchains evaluate sensing data quality, but malicious requesters can manipulate requirements, causing evaluation failures.
• Blockchain transparency allows requesters with node control to access data without payment, undermining fairness and worker motivation.

Purpose of the Study:

• To propose a novel crowdsensing scheme addressing malicious requester manipulation and data access issues.
• To ensure the confidentiality and integrity of sensing data, accessible only to authorized workers and requesters.
• To implement and evaluate a prototype of the proposed crowdsensing solution.

Main Methods:

• Utilizing Trusted Execution Environments (TEEs) within a blockchain framework for crowdsensing.
• Implementing smart contracts for automated quality evaluation and payment processes.
• Developing a prototype to demonstrate and assess the performance of the proposed scheme.

Main Results:

• The proposed scheme effectively addresses the challenge of malicious requesters manipulating quality evaluation.
• Confidentiality and integrity of sensing data are maintained, ensuring secure access for workers and requesters.
• Performance evaluation indicates that the solution guarantees fairness with minimal overhead.

Conclusions:

• The novel crowdsensing scheme using TEEs successfully ensures fairness and data security.
• The solution mitigates issues of unfair dealing and enhances worker participation motivation.
• The practical implementation demonstrates the feasibility and efficiency of the proposed approach.