Transparent and Privacy-Preserving Mobile Crowd-Sensing System with Truth Discovery

Area of Science:

• Computer Science
• Information Security
• Distributed Systems

Background:

• Mobile crowd-sensing (MCS) systems are increasingly popular due to the proliferation of portable devices.
• Traditional MCS systems often lack transparency and verifiability, allowing data manipulation and privacy breaches.
• Existing privacy-preserving solutions in MCS fail to provide public verifiability for truth discovery services.

Purpose of the Study:

• To propose a novel transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS).
• To enable data requesters to verify the accuracy and integrity of sensing data.
• To ensure the privacy of participants in MCS systems.

Main Methods:

• Development of a TP-MCS scheme utilizing zero-knowledge proof (ZKP) and Merkle commitment trees.
• Implementation of protocols for secure data aggregation and truth discovery.
• Theoretical analysis and experimental validation of the proposed scheme's security and efficiency.

Main Results:

• The proposed TP-MCS scheme effectively ensures data privacy for participants.
• Data requesters can accurately verify the correctness of the truth discovery service.
• The system demonstrates strong security guarantees and efficient performance through analysis and experiments.

Conclusions:

• The developed TP-MCS system successfully addresses the transparency and privacy challenges in mobile crowd-sensing.
• The integration of ZKP and Merkle trees provides a robust solution for verifiable and private data aggregation.
• This approach enhances trust and facilitates wider adoption of MCS technologies.